A Model for the Implementation of Non-Formal Language Acquisition in Language Revitalization: the Case Study of Irish

A Model for the Implementation Of Non-Formal Language Acquisition In Language Revitalization: The Case Study of Irish

Anne Beltman 12299448 MA Thesis Language and Society University of Amsterdam Supervisor: prof. F. Gobbo 06-08-2019

Acknowledgements

I would like to thank Prof. dr. Gobbo, my thesis supervisor, for his guidance throughout this project. In agreeing to take on this project, he has taught me a lot about sociolinguistics, but also about academia itself. I would like to thank my friends and family for all their support, for proofreading all the drafts I sent and calming me down in cases of panic. Finally, my thanks to Fien, Kim, Zach and Jenna for being there always.

Abstract

Non-formal language acquisition (NFLA) is language acquisition that is embedded in planned activities that are not explicitly designated as learning and is intentional from the learner’s point of view (Colardyn & Bjornavold, 2004). Using existing frameworks, a model was created to test whether a threatened language has enough support to implement NFLA- based programs, in order to help revitalization of the language. To test the proposed model, it has been applied to Irish. From this case study, it can be concluded that while the model provides a good basis, there are still improvements to be made.

Table of contents

1. Introduction 6 1.1. Background 6 1.2. Relevance 7 1.3. Research question 8 2. Methodology 10 3. Theoretical Background 11 3.1 Non-formal language acquisition 11 3.2 Terminology 12 3.3 Language Revitalization 12 3.4 Frameworks 14 3.4.1. GIDS and E-GIDS 14 3.4.2. Edwards’ typology of minority-language settings 17 3.4.3. The Digital Language Vitality Scale 18 3.5. Concluding remarks 19 4. Proposed Model 20 4.1. Factors used 20 4.2. Coding 22 4.3. How to use the model 24 4.3.1. Example of the application of the model 24 5. Case Study: Irish 27 5.1. Historical Overview of the Irish language 27 5.1.1. Decline 27 5.1.2. Current situation 28 5.2 Revitalization 29 5.2.1. Early revitalization (1892 – 1999) 29 5.2.2. Revitalization 2000-2010 29 5.2.3. Revitalization after 2010 30 5.3 Application of the model 31 6. Discussion 36 6.1. Discussion of the model 36 6.2. Case study 37 6.3. Research Question 37 6.4. Limitations of the study 37 6.5. Further research 38 6.6. Concluding remarks 38 7. References 39

8. Webography 42 9. Appendix A - Scoresheets 43 9.1. LVE scoresheet 43 9.2. Digital Language Vitality scoresheet 45

1. Introduction

1.1. Background In education, a distinction can be made between three different forms of learning: formal, informal and non-formal. Formal learning consists of all learning that takes place in institutional settings, while non-formal and informal learning both consist of learning taking place outside of institutional settings. The difference between these two forms is that non- formal learning is intentional from the learners’ perspective, while informal learning is not intentional (Colardyn and Bjornavold, 2004b). These forms of learning can also be applied to the learning of languages. Formal language learning is language education in a classroom setting, by (qualified) teachers. The informal learning of language is for example the learning of words from television, while non-formal language learning is for example someone learning a language with the help of a textbook, but not in a classroom setting. In the last three decades, there have been several significant developments in technology, which has had major consequences for non-formal language acquisition. For example, the Internet being more accessible to people everywhere made online language learning more accessible (Colardyn and Bjornavold, 2004b). Nowadays, the main examples of non-formal language acquisition are websites and apps like Duolingo, Busuu, Memrise or the aptly named Rosetta Stone. These apps all share the fact that the learning is noncommittal. Users can learn at their own pace, skip lessons they find unimportant, or repeat previous lessons as frequently as they like. Apps like Duolingo offer established languages with millions of speakers worldwide like Spanish and English, but also languages that are considered endangered, or minority languages, like Irish, Welsh, Catalan and even Navajo (“Duolingo”, March 12th, 2019). According to the website, 973.000 users are actively learning Irish, and 330.000 users are actively learning Welsh (“Duolingo”, March 12th, 2019). Compared to the last recorded number of speakers, 1.2 million and 570.000 respectively (Eberhard, Simon and Fennig, 2018), these are staggering numbers. While this proves that there is a significant number of learners of these languages, the question is whether all these learners will eventually become speakers, and if so, what type of speaker they will become. Gobbo (2019) identifies several types of speakers: old fluent speakers, young fluent speakers, semi speakers, terminal speakers, ghost speakers, neo-speakers and rememberers. The first two types, old fluent speakers and young fluent speakers are speakers who experience no language loss due to an abundance of conversation partners. The third type, semi speakers, are speakers who have (some) receptive skills, but little to no productive skills. The fourth type consists of terminal speakers, who have limited receptive and productive skills. Type 5 is ghost speakers, speakers who are fluent in a language, but deny having this knowledge. Revitalization programs can cause two new types of speakers to emerge: neo-speakers and remembers. Neo-speakers are speakers who identified as semi or terminal speakers, but who have regained both receptive and productive skills throughout the revitalization program. Finally, remembers are speakers who reacquire their language after a long period of severe language loss (Gobbo, 2019). Language revitalization can be seen as mainly focusing on two important issues: 1) it aims to increase the number of speakers, and 2) it ensures that these speakers transmit the language to the next generation (intergenerational language transmission) (Fishman, 2001). Non- formal language acquisition can be used to achieve the first goal to increase the number of speakers in two ways. First, it can introduce the language to new learners, people with no knowledge of the language but who are interested in learning. Second, it can help semi-

speakers or ghost-speakers to reacquire or improve their language capabilities (Gobbo, 2019). This implies that the effect of language revitalization is dependent, among other factors, on the type of speaker the program targets, but also the type of speaker it creates. Ideally, a program would create young fluent speakers, as this is necessary for intergenerational language transmission. The question we can draw from this, is how can non-formal language acquisition help with revitalizing a language? 1.2. Relevance When the mainstream usage of internet began, in 1998, there was virtually no online linguistic diversity: 75% of all webpages were in English, 11% was in Spanish, French, Polish, German or Portuguese, and the final 14% of webpages were in all the other languages of the world combined. This changed rapidly: in 2005, only 45% of all webpages were in English. Spanish, French, Polish, German and Portuguese together accounted for 21% and the category ‘Rest’ now made up 34% (Pimento, Prado & Blanco, 2009). Still, the online linguistic diversity is growing. Major websites like Google are available in more and more languages, not just in official languages, but also in languages that have for a very long time lacked official support, such as Maori ("Languages - Google Translate", n.d.). Large media corporations acknowledge non-standard languages, such as the BBC with its decision to start broadcasting services in Pidgin in West-Africa ("BBC Pidgin service launched in Nigeria", 2017). Additionally, to support the online presence of languages with different characters or scripts than English, the Unicode Standard was developed. This standard, developed by the Unicode Consortium, consists of a database with encodings for all characters from all written languages, where each unique character is assigned a unique code. This can then be used to encode multilingual plain text, which is useful when text files are exchanged internationally ("The Unicode® Standard: A Technical Introduction", 2018). There are also scientific efforts to promote online linguistic diversity. The Digital Language Diversity Project (DLDP) was created to “advance the sustainability of Europe’s regional and minority languages in the digital world by empowering their speakers with the knowledge and abilities to create and share content on digital devices using their minority languages” (www.dldp.eu, April 13th 2019). The project focused on four major points of action. The first goal was to acquire information about the actual linguistic diversity in Europe in general, and more specific information about the digital use of Basque, Breton, Karelian and Sardinian. This was achieved through the creation and distribution of a survey on digital use and usability of regional and minority languages. The second goal was to develop a training program that could be applied throughout Europe, which would stimulate speakers of regional and minority languages to create both digital content as well as language learning materials in their language. In addition, the project created so-called ‘Digital Language Survival Kits’. These Kits are tools for speakers of regional and minority languages who wish to make their language fit for digital use. They contain recommendations on this process, such as possible challenges or difficulties and advice on available aids, as well as a tool to self- assess the digital fitness of the language. Finally, the fourth objective was to create ‘A Roadmap to digital language diversity’. This document, mainly aimed at policy-makers and other stakeholders, outlines challenges that languages face and gives solutions for these challenges, in order to create more digital language diversity in Europe ("Project | The Digital Language Diversity Project", n.d.). The DLDP came to its natural end in 2018. At this point, all four short-term objectives as described above had been achieved. The survey on digital use and usability of regional and minority languages led to the development of a ‘Digital Language Vitality Scale’, which can be used to assess the digital presence of a language, and which will be discussed later. The creation of the training program was successful, and it can now be obtained from the project’s website by anyone who wishes to know how to increase the online presence of their

language. There are two types of Digital Language Survival Kits available, one that is generally applicable to all languages, and Kits for specific languages: Basque, Breton, Karelian and Sardinian. Finally, a Roadmap was developed, containing recommendations to ensure legislation on the European level and calling for an extension of the DLDP, to protect and expand the digital language diversity ("DLDP Results | The Digital Language Diversity Project", n.d.). A similar project is MIME (Mobility and Inclusion in Multilingual Europe) project, which consisted of 25 teams from 22 institutions conducting research into issues surrounding policy regarding multilingualism in the EU. This project was unique in that researchers did not focus on one specific angle, but instead worked together with researchers from other disciplines in order to create a comprehensive treatment of linguistic diversity. MIME focused on questions like: “How can Europeans balance the requirements of mobility in a modern, integrated, technologically advanced society with the need to maintain and take advantage of Europe’s linguistic and cultural diversity?” and ”What does this challenge imply in terms of communication practices, language use and language rights, language teaching and learning and how does this translate into policies regarding national languages, minority languages, and immigrant or heritage languages?” (MIME Vademecum, 2018). The project, which was funded by the European Union and ran from 2014-2018, has generated a large amount of output. Scientific articles have been published in a variety of different journals, and several different book chapters, special journal issues and general progress reports have been produced. In addition to this, a Vademecum was created. This document “aims to help users confront the challenges of linguistic diversity as a large-scale social issue, and to equip them with tools with which they can identify the cornerstones of a policy plan for their own language policy, in their own context, matching their own needs” (MIME Vademecum, 2018). To fulfill this goal, the Vademecum answers 72 questions regarding 6 topics: Language Policy Analysis; Minorities, Majorities and Language Rights; Linguistic Diversity, Mobility and Integration; Language Education, Teaching and Learning; Translation, Language Technologies and Alternative Strategies; Special Topics. The project’s key results were the conclusion that language policies are necessary and justified, and that they must consider the combination of Mobility and Inclusion, which will lead to social cohesion (MIME Vademecum, 2018). The mere existence of these projects indicates that there was an increase in attention for the position of minority languages in contemporary societies. The combination of this increase in attention and support for minority languages and endangered languages from businesses, research institutes and policy makers, and new technological developments has created possibilities for the development of revitalization programs that are based on non-formal language acquisition. Some of these programs are developed for the sole purpose of gaining more speakers, which in turn could have real-life implications, as an increase of speakers could alter the status of the language as portrayed in Lewis & Simons (2010). Therefore, it is important to find out whether languages are suitable for implementing these types of programs. If there is enough support, this could be used as a signal to policymakers that NFLA programs can be implemented. However, implementing these programs without enough support is likely to be a waste of time, and means that revitalization efforts should focus on other ways to improve speaker numbers. 1.3. Research question The situation sketched above, that it is useful to know the amount of support for NFLA-based programs, led to the following research question: “in what cases can non-formal language acquisition be used in language revitalization?” This paper will not look at the success of revitalization programs, as that is dependent on too many variables. Instead, it will test

whether enough facilities are present for non-formal language acquisition-based programs to be used. To answer this question, a model will be developed which can be used to test languages for the amount of support. This thesis is structured as follows. Section 2 will further elaborate on the methodology as briefly discussed above. Section 3 will cover some basic terminology and discuss relevant frameworks. Section 4 contains the proposed model, which will be tested using a case study in section 5. In section 6 the model and the case studies are discussed, and section 7 will state the conclusion.

2. Methodology

Language Revitalization is a complex process with many factors involved, such as the current number of speakers and status of the language, but also its history, economic and digital vitality, and many more factors. While being able to predict whether a program aimed to revitalize a language will be successful in achieving the goals set by official institutions or language activists would be of great help, this would be virtually impossible due to the vast number of factors involved. Even if possible, such a task would certainly exceed the scope of this study. Instead, this study will focus whether the language that is being revitalized has sufficient measures in place for non-formal language acquisition-based (NFLA) programs to be implemented. In order to construct this model, four of the most used and most relevant frameworks regarding typology of languages and language revitalization will be discussed: GIDS (Fishman, 2001), E-GIDS (Lewis & Simons, 2010), the Digital Language Vitality Scale (Soria e.a., 2017), and the Sociology-of-Language Framework for Minority (and other) Languages (Edwards, 2010). These frameworks have been chosen because they are some of the most comprehensive works in the field of language revitalization, and the most used. The abovementioned frameworks will be critically discussed in section 3.4, and the relevant and useful components will be selected and combined into a new model. In order to test the validity of the proposed model, it will be applied in a case study: the revitalization of the Irish language. Case studies are useful in this case because it will show both the merits and the shortcomings of the model. For this case study, Irish was chosen, a choice which will be expanded upon in section 5. The case study will first give a historical overview of the decline and revitalization of the language before applying the model and drawing conclusions about the possibilities for implementing NFLA based revitalization programs for the Irish language.

3. Theoretical Background

3.1 Non-formal language acquisition Education is considered to be one of the pillars of human society, and has therefore been studied immensely, on micro-levels, macro-levels and everything in between1. One of the areas of education in which research has been conducted is the different types of learning that students use, both within and outside of the educational system. From early on, a distinction was made between formal learning - all forms of learning that take place in institutional settings - and informal learning - which consisted of all learning taking place outside of institutional settings. Scribner and Cole (1973) argued for a distinction within formal learning, namely between formal learning that is school-based and formal learning that takes place in everyday life. To make a distinction between these two types of learning, they named it school-based formal learning and non-institutional formal learning (Scribner and Cole, 1973). The name non-formal education was created by Coombs and Ahmed (1974), who used ‘education’ instead of ‘learning’. In literature after this year, both terms are used interchangeably, as they are considered the same (Mok, 2011). Colardyn and Bjornavold (2004b, p. 71) describe the different types of learning as follows: “Formal learning consists of learning that occurs within an organized and structured context (formal education, in-company training), and that is designed as learning. It may lead to a formal recognition (diploma, certificate). Formal learning is intentional from the learner’s perspective. Non-formal learning consists of learning embedded in planned activities that are not explicitly designated as learning, but which contain an important learning element. Non- formal learning is intentional from the learner’s point of view. Informal learning is defined as learning resulting from daily life activities related to work, family, or leisure. It is often referred to as experimental learning and can to a certain degree be understood as accidental learning. It is not structured in terms of learning objectives, learning time and/or learning support. Typically, it does not lead to certification. Informal learning may be intentional but, in most cases, it is non-intentional (or ‘incidental’/random).” This framework is accepted by researchers and major institutions alike. Both UNESCO and the European Union actively promote non-formal learning, because they see it as a way to achieve their focus point of lifelong learning. Cedefop, the European Centre for Development of Vocational Training, published a manual ‘The European Guidelines for Validating Nonformal and Informal Learning’, in which they provide organizations and policymakers with tools to implement nonformal and informal learning opportunities (European guidelines for validating non-formal and informal learning, 2016). However, not all researchers agreed with these definitions, or even the need for the term non- formal learning. The main argument against the term seems to be that the boundaries between the different types of learning are not very clear. In the debate around this term, a quote from McGivney has often been repeated: “It is difficult to make a clear distinction between formal and informal learning as there is often a crossover between the two” (McGivney, 1999, p1). If the boundaries between formal and informal learning are vague, adding a third type of learning is not likely to clarify matters. Based on McGivney’s work,

1 Parts of section 3.1 are an an elaboration of the author’s final paper for the class ‘L2 in the Classroom’.

Colley, Hodkinson & Malcolm (2002) sought to “examine and analyze a wide range of relevant literature about formal, non-formal and informal learning, in order to provide greater conceptual clarification” (p2). They discussed several models and definitions regarding the distinction between formal, nonformal and informal learning, and drew up a list of criteria used to make this distinction. They conclude that it is impossible to draw a general, meaningful divide between the three types of learning, as this divide is based on the context in which the learning takes place and for what purpose. Instead, they argue that the focus should lie on how the different types of learning interrelate with each other. Finally, they call for an approach that takes into account the wider historical, social, political and economic contexts of learning (Colley, Hodkinson & Malcolm, 2002). Despite these criticisms, the general opinion among researchers seems to be in favour of non-formal language acquisition. 3.2 Terminology The focus of this thesis is on the revitalization of languages. In this field of research, there are several terms used to classify languages that are not used on a national level, or do not receive any institutional support: endangered languages, minority languages and contested languages. To create clarity, several definitions of these terms will be provided, along with the definitions that will be used in this thesis. While the aforementioned terms all refer to languages that receive little to no institutional support, there are salient differences between them, mainly in the number of speakers and the domains in which the language is used. The term ‘Endangered’ is quite consistently used to describe languages that are in danger of becoming extinct (Grenoble & Whaley, 2006; Austin and Salabank, 2011). These languages have a decreasing number of speakers and are often used in few domains. The European Charter for Regional and Minority languages gives the following definition of minority languages: “languages that are traditionally used within a given territory of a State by nationals of that State who form a group numerically smaller than the rest of the State's population; and different from the official language(s) of that State” (1992). In other words, a language spoken by a minority within a larger population that speaks a different language. Paulston (1998) states that the most important part of this definition is the second part: it is not the official language of that State. Contested languages are languages that are linguistically distant from the official language, and often have an established literary tradition and some form of standardization and corpus planning, but are still considered ‘dialects’ (Gobbo, 2019). 3.3 Language Revitalization Of the 7000 languages in the world, it is estimated that half of these are in danger of going extinct, as they are not being learned by children as their first language (L1). This is called ‘language shift’: a process where people in a community ‘shift’ to another L1 than the generations before them, usually because the new L1 is the dominant language. Language revitalization efforts are any efforts made by speakers, language activists, researchers or policy-makers to stop a language from actually going extinct (Austin & Salabank, 2011). In the 2003 report ‘Language Vitality and Endangerment’ (UNESCO, 2003), nine factors were identified that can be used to assess language vitality and state of endangerment, attitudes and the urgency for documentation. Also known as the LVE model (Language Vitality and Endangerment), these factors provide insight into the overall sociolinguistic situation of a language.

1. Intergenerational language transmission; 2. Number of speakers; 3. Proportion of the total population made up by speakers of the language in question; 4. Loss of existing language domains; 5. Response to new domains (including media); 6. Materials available for purposes of education and literacy; 7. Official language attitudes and policies; 8. Speakers’ own attitudes and policies; 9. Amount and quality of relevant documentation

Figure 1. UNESCO’s model for assessing language vitality (LVE). For each factor, a five-point evaluational scale has been developed (except for factor two). For example, for factor 6, a score of 0 means that there is no material at all available that can be used in education, not even an orthography. A score of 5 means there is an established orthography and established literary tradition (UNESCO, 2003). While this model was created to give an overview of important factors when assessing language vitality, the factors described can also be used as focus points for revitalization efforts. Fishman (1991) is considered one of the most influential researchers in the field of language revitalization. He concluded that languages were threatened because of language shift, where speakers would ‘discard’ their native languages to shift to a more dominant language. To stop a language from going extinct, this language shift had to be reversed. The only way to do this was to ensure intergenerational language transmission (Fishman, 1991). The topic of Reverse Language Shift has since been expanded, but the main principle has stayed the same: intergenerational language transmission should be the first goal of any revitalization efforts. Austin & Salabank (2011) argue that to achieve this, the first step is to increase the number of speakers. This can be done by teaching the language to people in the community that have no knowledge and by transforming semi- and terminal speakers into new speakers. The next step is to get these speakers to use the language in everyday communication in an increasing number of domains. This will, eventually, lead to intergenerational language transmission. The discourse surrounding language revitalization also uses other factors, like documentation and standardization of the language. While these are tools that should be used to achieve intergenerational transmission, they should not be the goal (Austin & Salabank, 2011). There is no one way to revitalize a language, as the success of revitalization is dependent on a large number of factors, like the percentage of speakers, speakers’ attitude, government policy, etc. Austin & Salabank (2011) distinguish between four types of revitalization: school- based, community-based, adult language learning and family-based. School-based revitalization efforts can include language classes, bilingual education and immersion schools. These programs are relatively successful for a number of reasons. As these programs take place at schools, they can reach a large group of language learners. These learners will then be taught the language while they are at the right age for this to take place most quickly and easily. Community-based programs often take the form of summer camps, where children are exposed to a great deal of input in the target language. Adult language learning is essential to revitalization but is also arguably the most difficult. It is often made harder by the lack of skilled teachers. Some universities offer courses in endangered languages, and there are several programs worldwide focusing specifically on adult language learning. The final type is family-based revitalization. For revitalization to succeed, a language must regain its place as a language of daily communication, and not just be a school language. Family-based revitalization programs provide support to families to help them in using the language at home. These four types cannot exist independently, rather they have to be used in

conjunction with each other. School-based programs alone are not enough to ensure intergenerational language transmission, family support for the language is necessary as well (Austin & Salabank, 2011). These four types correspond to the two main issues of language revitalization: increasing the number of speakers and ensuring intergenerational language transmission. School-based revitalization, community-based programs and adult language learning programs all focus on increasing the number of speakers, while family-based programs focus on intergenerational language transmission. The different types of programs can also be connected to the distinction in speaker types. School-based and community-based programs target children, i.e. learners and young speakers, while adult language learning programs target learners and semi-speakers, ghost speakers and terminal speakers. 3.4 Frameworks Within the field of language revitalization, several typologies and frameworks have been developed to classify languages. A number of these will be critically discussed below, and the selection of certain elements to use in the model will be justified. 3.4.1. GIDS and E-GIDS One of the most important frameworks used to classify languages is the Graded Intergenerational Dislocation Scale (GIDS), first created by Fishman in 1991 and later revised in 2001. This work uses the version from 2001, as it is the most recent one. Fishman reasoned that this scale could be used to locate in what areas the language shift had taken place (Fishman, 2001). This location would then help to establish and focus the priorities of any efforts to reverse this language shift, which would cause the language to not be threatened anymore. Figure 2 shows Fishman’s GIDS. Fishman uses Xish to indicate the ‘low’ language in a society, and Yish to indicate the ‘high’ language. Xmen/Ymen means speakers of the X and Y language respectively. Stages of Reversing Language Shift: Severity of Intergenerational Dislocation (read from the bottom up)

1. Education, work sphere, mass media and governmental operations at higher and nationwide levels 2. Local/regional mass-media and governmental services 3. The local/regional (i.e., non-neighbourhood) work sphere, both among Xmen and among Ymen 4. Public schools for Xish children, offering some instruction via Xish, but substantially under Yish curricular and staffing control 5. Schools in lieu of compulsory education and substantially under Xish curricular and staffing control 6. Schools for literacy acquisition, for the old and for the young, and not in lieu of compulsory education 7. The intergenerational and demographically concentrated home-family-neighbourhood: the basis of mother tongue transmission 8. Cultural interaction in Xish primarily involving the community-based older generation 9. Reconstructing Xish and adult acquisition of Xish

Figure 2. GIDS (Fishman, 2001)

The GIDS was the first framework of its kind and was used widely by researchers throughout the world. While it provided many new insights at the time of publishing, in the years since several shortcomings have become apparent. Lewis and Simons (2010) created an expanded model of the GIDS, called EGIDS, in which they have incorporated the proposed solutions to the shortcomings of the GIDS. This framework is shown in Figure 3.

LEVEL LABEL DESCRIPTION UNESCO STATUS 0 International The language is used internationally for a broad Safe range of functions 1 National The language is used in education, work, mass Safe media, government at the nationwide level 2 Regional The language is used for local and regional mass Safe media and governmental services 3 Trade The language is used for local and regional work Safe by both insiders and outsiders 4 Educational Literacy in the language is being transmitted Safe through a system of public education 5 Written The language is used orally by all generations Safe and is effectively used in written form in parts of the community 6a Vigorous The language is used orally by all generations Safe and is being learned by children as their first language 6b Threatened The language is used orally by all generations but Vulnerable only some of the child-bearing generation are transmitting it to their children 7 Shifting The child-bearing generation knows the language Definitely Endangered well enough to use it among themselves, but none are transmitting it to their children 8a Moribund The only remaining active speakers of the Severely Endangered language are members of the grandparent generation 8b Nearly The only remaining speakers of the language are Critically Endangered extinct members of the grandparent generation or older who have little opportunity to use the language 9 Dormant The language serves as a reminder of heritage Extinct identity for an ethnic community. No one has more than symbolic proficiency 10 Extinct No one retains a sense of ethnic identity Extinct associated with the language, not even for symbolic purposes Figure 3. Expanded GIDS (EGIDS). (Lewis & Simons, 2010). The EGIDS contains a total of 13 levels, whose numbers have been designed to match the numbers of the corresponding levels in the GIDS. Levels 0, 9 and 10 have been added to make the model more inclusive and applicable. Two columns have been added, the first shows the major functional category of that level, the other shows the corresponding level of the UNESCO framework. Level 0-3 mean that the language’s identity function is ‘Vehicular’, and that it has a clear level of official use, either international (level 0), national (level 1), regional (level 2) or non-official (level 3). Languages that are placed at level 4-6 have ‘Home’ as the main identity function and are being transmitted from parents to their children. A distinction is made based on literacy status, which can be Institutional (level 4), Incipient (level 5), or non-existent (level 6a). If a language is not transmitted between generations, the distinction is made based on the youngest generation that has some proficient speakers: children (level 6b), parents (level 7), grandparents (level 8a) or great-grandparents (level 8b). Finally, languages with a Heritage or Historical function will be categorized at level 9 and 10 respectively (Lewis &

Simons, 2010). An interesting question is whether non-formal language acquisition programs will create speakers who only use the language in digital domains, or if it will create speakers who will use the language outside of digital domains. The latter would mean that the language would classify at level 4, as it will have ‘Home’ as the main identity function, a literacy status and intergenerational language transmission. This would also mean that NFLA-based programs are successful in language revitalization, as they will have improved the status of the language, thereby moving it further away from endangerment. However, as mentioned before, any actual predictions about the success of NFLA-based programs are outside the scope of this study and will therefore remain speculation. While the E-GIDS is considered one of the most important frameworks within the field of language revitalization, it is not without shortcomings. It does not pay attention to the number of speakers, either absolute or relative, nor does it consider community language attitudes. Compared to the UNESCO Language Vitality Scale, the levels are not nuanced, as they only consider a how a language is used in a society, without looking at factors like education, media or documentation (Romaine, 2017). The EGIDS, like the GIDS, assumes language shift in a downward direction, i.e. languages move to a lower level. For example, if a language is not used effectively anymore in written form, it will move from status 5 to status 6a, thereby moving closer to being a threatened language (status 6b). However, in the case of language revitalization, languages often move upwards, because of intervention and revitalization programs. This situation warrants a different set of labels and level descriptions. For this situation, the EGIDS has been relabeled to facilitate categorization of languages that are currently in the process of revitalization, which is shown in figure 4.

6a Vigorous The language is used orally by all generations and is being learned at home by all children as their first language. 6b Re-established Some members of a third generation of children are acquiring the language in the home with the result that an unbroken chain of intergenerational transmission has been re-established among all living generations 7 Revitalized A second generation of children are acquiring the language from their parents who also acquired the language in the home. Language transmission takes place in home and community 8a Reawakened Children are acquiring the language in community and some home settings and are increasingly able to use the language orally for some day-to-day communicative needs 8b Reintroduced Adults of the parent generation are reconstructing and reintroducing their language for everyday social interaction. 9 Rediscovered Adults are rediscovering their language for symbolic and identificational purposes Figure 4. Revitalization EGIDS levels (Lewis & Simons, 2010). The most important part of this relabeled EGIDS is the focus on the generation that is acquiring the language. During the process of language shift, one of the units of measurement is the youngest generation that still has some proficient speakers. If the only proficient speakers belong to the grandparent generation, that means that the language shift has progressed further than if the parent generation still has proficient speakers. However, during the process of revitalization, the success is measured by identifying the oldest generation that still has proficient speakers. Level 6a, Vigorous use, is achieved only when all generations are using the language again, and it is being transmitted intergenerationally in the home setting (Lewis & Simons, 2010).

Seeing as this model is concerned with language revitalization, instead of including the regular EGIDS it will include the relabeled EGIDS. The categories from the relabeled EGIDS are more suitable for categorizing language revitalization programs, which is what is necessary for the model. 3.4.2. Edwards’ typology of minority-language settings Around the same time Lewis and Simons developed the EGIDS, Edwards created his own typology of minority-language settings (2010). First, he discusses several other frameworks and typologies, from Ferguson, Stewart, Kloss, Haarman and others, explaining the advantages and disadvantages from each model, which he then tries to incorporate into his own model. Edwards’ typology considers several variables, that have been put in one of three basic categories: speaker, language and setting. Variables concerning speakers are for example the number of speakers, but also their age, socioeconomic and educational status and language attitudes. Language variables include variables like the (in)stability of bilingualism in the region, domains in which the language is used and degree of standardization and modernization. Finally, the category setting includes variables such as degree of transmission, amount of institutional support and nature of migration. For each of these three categories, questions are developed pertaining to eleven different disciplinary perspectives: demography, geography, economics, sociology, linguistics, psychology, history, politics/law/government, education, religion and the media. A cross-tabulation of the three categories and eleven disciplinary perspectives provides Edwards’ typology, as shown in figure 5.

Disciplinary Perspective Speaker Language Setting 1. Demography 1 2 3 2. Geography 4 5 6 3. Economics 7 8 9 4. Sociology 10 11 12 5. Linguistics 13 14 15 6. Psychology 16 17 18 7. History 19 20 21 8. Politics-Law-Government 22 23 24 9. Education 25 26 27 10. Religion 28 29 30 11. Media 31 32 33 Figure 5. Edwards’ typology of minority language settings. Every number has a corresponding question. Edwards created a list of these questions, but emphasized that these are not specific enough, but should be seen as points of departure (Edwards, 2010). These questions can be used by researchers and language activists alike to determine relative strengths and weaknesses in a minority language, which can then be targeted specifically by revitalization efforts. While Edwards’ typology is very well-rounded, there is considerable overlap with UNESCO’s Language Vitality and Endangerment model (LVE) The perspectives Demography, Psychology, Politics/Law/Government, Religion and Media are all covered under the LVE, so are not needed for the present model. The perspectives Geography, Sociology and History are very relevant in classifying a language, but not in classifying revitalization efforts. From this typology, three factors are relevant and useful in this model: the type of school support for the language, the association between language and economic success/mobility and the degree of language standardization.

3.4.3. The Digital Language Vitality Scale The Digital Language Diversity Project (DLDP) has created both a Digital Language Vitality Scale (DLVS) and a Roadmap to Digital Language Diversity. Digital language vitality is defined as ‘the extent to which a language is present, used and usable over the Internet and digital devices’ (Soria e.a., 2017, p2). Nowadays it is important that a language can be used digitally. After all, more and more communication takes place through (digital) technology. If a language cannot be used digitally, it cannot be used in a number of domains, which could have negative consequences for the revitalization. For this reason, the Digital Language Vitality Scale has been included in the model. In order to measure the digital vitality of a language, both a scale and indicators were developed. The scale, which is based on the classification by Kornai (2013) and Gibson (2015), consists of six levels:

1. Pre-digital - the language either has no writing system at all or no digital encoding, people’s digital proficiency is very low; 2. Dormant - the language could be digitally used with the right technological support; 3. Emergent - speakers are mildly digitally literate, there are some basic language resources available but there is limited technological support; 4. Developing - there is some usage over in (social)media, there is a medium-sized Wikipedia; 5. Vital - there is regular language use in all digital domains, most used operating-systems and software is available; 6. Thriving - the language is used extensively and without any technological barriers in all current digital domains, the latest technology is available (Soria e.a., p4). In order to assess where a language falls on the scale, a set of indicators has been provided, divided into three sections. The first section of indicators, called Digital Capacity, assesses whether the necessary technological infrastructure is available to support the use of the language in the digital world. Examples of indicators from this section are the digital literacy of a community and the availability of digital language resources. The second section is called Digital Presence and Use, and gages the actual digital use of a language: in what domains is it used and to what extent? It uses indicators such as whether a language is used on social media and the availability of Wikipedia pages in that language. The final section is Digital Performance, which refers to what can be done with a language digitally. This section seems similar to Digital Presence and Use, but where that section focuses on where a language is used, this section focuses on with what purpose a language is used. Indicators used are the available Internet Services and Machine translation tools. A complete overview of all indicators is given in figure 6.

Indicator 1. Evidence of connectivity 2. Digital literacy 3. Internet penetration or digital population size Digital capacity 4. Character/script encoding 5. Availability of language resources 6. Use for e-communication 7. Use on social media Digital presence and use 8. Availability of Internet media 9. Wikipedia 10. Available Internet services 11. Localized social networks 12. Localized software Digital performance 13. Machine translation tools/services 14. Dedicated Internet top-level domain Figure 6. Indicators used for the Digital Language Vitality Scale (Soria e.a., 2017).

3.5. Concluding remarks In this section, a theoretical background was provided, on which this study is based. First several important terms were explained, in order to provide a framework from which this study has been carried out. Then, four relevant frameworks were discussed. Both the explained terminology and the abovementioned frameworks represent only a small part of the complete field of research into language revitalization. The limited scope of this study did not allow room for more, nor was it necessary. As a consequence, there has been little critical discussion, However, this is not necessarily a problem. In regard to the important terms, the definitions given can be considered as widely-accepted. The discussed frameworks are considered as some of the most comprehensive frameworks available and are among the most used. For these reasons, the lack of critical discussion on the discussed terms and frameworks can be overlooked. From section 3.4, it can be concluded that the discussed frameworks are either very specific and focus only on one aspect (DLVS) or are more general but still only focus on the status of a language (E-GIDS, LVE, Edwards’ framework). None of the frameworks focus on the actual process of language revitalization. Hopefully, the model that will be proposed in section 4 can change that and ensure that similar models are developed and used in the future.

4. Proposed Model

In the sections above, the most relevant frameworks have been discussed, and parts of those frameworks have been selected for use in the proposed model, which is shown below. First, all factors will be expanded on, as well as how they are scored. Then the final model will be presented, along with a guide on how this model should be used. 4.1. Factors used

STATUS DIGITAL ECONOMY SCHOOL LVE SCORE VITALITY SUPPORT SUPPORT 9. Rediscovered Predigital None None 1.4-1.9 8b. Reintroduced Dormant Slight Slight 2.0-2.4 8a. Reawakened Emergent Considerable Considerable 2.5-2.9 7. Revitalized Developing Numerous Numerous 3.0-3.4 6b. Re-established Vital Strong Strong 3.5-3.9 6a. Vigorous Thriving Complete Complete 4.0-4.4 Figure 7. Factors and categories of the proposed model. Figure 7 shows an overview of the factors and categories used in the model. The first factor in this model is the status of the language in question. Status is an important factor, which provides a good starting point for the rest of the model and is therefore the first factor. Since this factor does not occur in the LVE or in the Digital Vitality Scale, it was decided to incorporate this factor on its own. The E-GIDS is the most comprehensive framework to determine the status of a language, so it's only logical that it should be used here. As this model concerns language revitalization, the levels and labels of the Revitalization E-GIDS have been used. The second factor is the digital vitality of the language. In section 3.4.3. this scale has been discussed, as well as why digital vitality is so important for our purposes. Because of this importance, the factor has been included in the present model. To determine the digital vitality, the indicators as stated by Soria e.a. (2017) will be used. These indicators, like the LVE, are assessed using evaluational scales. In contrast to the LVE, these evaluational scales do not all have the same amount of points. Instead of calculating an average score, the given evaluational scales will be used to assess where the language falls on the Digital Vitality Scale. The scoresheet for the digital vitality, which includes an overview of all the indicators with their evaluational scale, has been included in Appendix A. For the next two factors, scaling is not based on an existing framework, but rather on scales developed by the author. These scales also consist of 6 points, in order to correspond with the number of scales of the other factors and to offer nuanced scales. The third factor is the association between language and economy. In many cases, a minority language is associated with low socio-economic classes, while the dominant language is associated with economic progress. This model distinguishes between six possible types:

1. There is no economical support. The language is heavily stigmatized as being ‘low class’. Speakers are associated with menial jobs and low socio-economic backgrounds. 2. There is some economical support. The language is slightly stigmatized. Speakers are associated with menial jobs and low socio-economic backgrounds. 3. There is considerable economical support. The language is barely stigmatized. Speakers are somewhat associated with menial jobs and low socio-economic backgrounds. 4. There is numerous economical support. The language is not stigmatized. Speakers are somewhat associated with middle-class jobs. 5. There is strong economic support. The language is not stigmatized. Speakers are strongly associated with middle-class jobs and socio-economic backgrounds. 6. There is complete economical support. The language is not stigmatized. Speakers are not associated with a specific type of job. The language is used by workers from all socio-economic background. The fourth factor is the amount of school support a language receives. This factor is based on the Typology of Minority-Language Settings (Edwards, 2010), as discussed in section 3.4.2. Edwards stated three possible questions regarding the minority language in the educational system, which have been combined here to form the factor ‘School Support’. If there is little to no school support for a language, it will be very hard to implement school-based programs, which can be very influential on the success of the revitalization. This model measures school support on a 6-point evaluational scale: 1. There is no school support. The language is not used in any of the schools in the region/country it is spoken. There are no materials available to use the language in schools. 2. There is some school support. The language is used, to some extent, in a small number of schools in the region/country. There is some material available. 3. There is considerable school support. The number of schools that use the language is increasing, but still less than 50% of the total number of schools. 4. There is numerous school support. Over 50% of schools use the language, to some extent. There is material available, but not yet enough. 5. There is strong school support. The language is used, to some extent, in most of the schools. There is enough material available. 6. There is very strong school support. The language is used, to some extent, in all of the schools in the country. There is enough material available. The final factor is the LVE score. The LVE, which was discussed earlier, provides insight into the overall sociolinguistic situation of a language. By focusing on several important factors like language attitude and domains, the LVE is essential in any model on language revitalization. However, taking up all nine factors in the model would leave little space for other factors. As discussed in section 3.3., for every factor but the second one, a five-point evaluational scale has been developed. This means that for eight factors, a language can score between 0 and 5. In order to still take all factors into account, the choice has been made to use the average score. In order to calculate this average, a scoresheet has been developed, which can be viewed in Appendix A. This means that factor 2, the number of speakers, will not be taken into account. However, as this factor is included in other ways in both the status of the language, and other factors of the LVE score, this is not considered a problem. As all factors use a 5-point evaluational scale, the average score will lie in between 0 and 5. However, not all possible averages are used in the model, only averages between 1.5 and 4.4. The reason behind this, is that for a language to score an average of 0, it will have to score 0 on all factors. This means that there are no speakers left, there is no orthography available

and there is no desire to use the language. In this case, instead of revitalization, reintroduction of the language is necessary, for which this model is not suitable. On the other end of the spectrum, scoring higher than 4.4 indicates that there is little to no need for revitalization, as the language is stable, receives official support and is transmitted through generations. The exclusion of scores lower than 1.5 and higher than 4.4 leaves 30 possible scores. As the model is on a 6-point scale, the scores have been grouped in 6 cohorts of 5, so 1.5-1.9, etc. 4.2. Coding The aim of this model is to determine whether revitalization programs that use non-formal language acquisition can be implemented for a specific language. In the section above, all factors have been explained and justified. The final step is to compile these factors into one cohesive model. For this purpose, every point from every factor has been analyzed: what effect will this point have on the possibility for non-formal language acquisition-based programs? A 5-point evaluational scale has been used to determine this effect, with colours instead of numbers:

Figure 8. Coding of the proposed model Combining this scale with the basic model gives the following proposed model:

Figure 9. Proposed model. The visualization of the factors in this way, can imply that the model is linear and that the factors are dependent on each other. However, this is not the case. The factors are merely

presented like this for clarity. Below, the coding of the factors will be explained. In section 4.3, the use of the model will be expanded upon, with an example. Status Figure 8 shows that Status 6a and 6b are both coded 5. Languages that fall in these categories are used orally by at least three generations, which means that there is an unbroken chain of intergenerational language transmission. This is a good starting point to implement NFLA programs, as there is already a base group of speakers. 7 and 8a are both coded 4, languages in these categories are acquired by some children, so there is some form of intergenerational language transmission, but it is not as strong as 6b and 6a. 8b is 3, in this category adult speakers are reintroducing the language into everyday social interaction. While NFLA programs could be implemented, there is little support. Finally, 9 is coded 2, as the language is being rediscovered, but only for symbolic and identificational purposes. Implementing NFLA programs will be very hard, as there is no language transmission. Because the language is being used more and more, meaning there is some support, it is coded 2, instead of 1. Digital Vitality The first category, predigital, is coded 1 as it indicates that there is no digital presence of the language. Nowadays, a digital presence is an important factor in successful revitalization, as a significant proportion of everyday interactions are held through digital technology. Having no digital presence will therefore indicate very low support for NFLA based programs, as these are often digital programs. ‘Dormant’ is coded 2, as this implies that the language could be used digitally, if provided the necessary support. ‘Emergent’ and ‘Developing’ are both coded 3, as these categories indicate that there is a digital presence, but it is not yet present enough to offer significant support for NFLA programs. ‘Vital’ is coded 4 as there is still some room for improvement, and ‘Thriving’ is coded 5 as in this category there are no barriers to digital use of the language. Economy As stated above, the factor economical support is graded on a 6-point evaluational scale: no support, some support, considerable support, numerous support, strong support and complete support. The category ‘no support’ is coded 1: if a language is heavily stigmatized and connected to menial jobs and ‘low class’, there will be little support for NFLA based programs, as learners’ intention to learn the language is a necessary factor in these types of programs. The category ‘some support’ is coded 2, as there is still some stigmatization. The categories ‘considerable support’ and ‘numerous support’ are coded 3. These categories imply a significant amount of support, which will not have such a strong negative effect, but rather a neutral effect. ‘Strong support’ and ‘complete support’ are coded 4 and 5 respectively. If the language is associated with ‘middle class’ or higher, this will indicate a (strong) support for NFLA programs. School support Like the association between the language and economic health, the amount of school support a language receives is graded on a 6-point evaluational scale: no support, some support, strong support or complete support. Coding these categories is more complicated, as NFLA programs do not necessarily involve the educational system. Therefore, the question can be raised why this factor is included at all. However, school support is a great indicator of attitudes towards a language. The category ‘no support’ is coded 1, as this indicates a negative attitude towards the language in the educational system, which does not provide a good basis for NFLA based programs. ‘Some support’ is coded 2, there is some support but not enough to have a neutral or positive influence. ‘Considerable support’ and ‘numerous support’ are

both coded 3, as these categories imply a significant amount of support, predicted to have a neutral effect. ‘Strong support’, meaning support in most of the schools, will provide a decent basis for NFLA based programs and is coded 4, while ‘complete support’ will provide a strong basis and is therefore coded 5. LVE-Score Starting from the lowest possible average, the first two categories are coded 1, as this average indicates a low score on the LVE scale. An average below 2.4 means that there are few speakers, its use is restricted and there is little support for maintenance of the language. This does not offer any support for revitalization in general, let alone NFLA programs. One category up, an average between 2.4 and 2.9, is coded 2, as this indicates a slight increase in support, but still not enough to warrant a higher score. An average between 3.0 and 3.4 is coded 3, this indicates a neutral effect. Averages higher than 3.4 are considered to have a high support for NFLA programs, with averages higher than 3.9 offering very high support.

4.3. How to use the model The proposed model uses a wide variety of information. When using the model, the first step is to collect all the information needed to assign the scores. The second step is to actually assign a score to every factor, using the model given above. The third and final step would be to combine the different factors to make a prediction about the success of the revitalization program. To draw a conclusion, all five factors should be taken into account. However, these factors are independent one from another. If one factor scores badly on the model, in category 1 or 2, this does not immediately mean that NFLA programs should not be used. Likewise, if one factor scores very well, in category 4 or 5, this does not mean that there is definitely enough support for NFLA programs. Rather, the different categories should paint an overall picture, whereby positive influences should outweigh negative influences. Meaning, the overall score of a program should at least be 3, preferably leaning towards 4 or even 5. Language revitalization is not a static process. During this process, many factors might change, which will also affect how a language will score on the model. For example, at the start of a revitalization process people’s attitudes towards the language might be very negative, but after a period of time this might have changed. This in turn will affect the LVE score of a language, which will affect whether there is enough support to implement NFLA programs. A language’s score should thus be evaluated periodically. Besides determining whether NFLA programs should be implemented, this model will also give insight into areas which might negatively influence this implementation. For example, if a program scores well overall, but badly on the factor ‘association with economical health’, this would be an area towards which policy should be directed, to improve the standing. 4.3.1. Example of the application of the model This section will demonstrate the use of the model. A fictional language, language X, will be used for this purpose. As mentioned above, the first step is to gather all the necessary information and to determine in which of the categories the language falls. The choice has been mode not to display all the information used in applying the model to language X, as that would take up too much space. Instead, for every factor a short summary will be given as to why the language falls in that category.

Status Language X is currently being acquired at home by a second generation of children. The parents of these children also acquired the language at home, indicating that a chain of language transmission in the home has been established. For this reason, language X falls under category 7, ‘Revitalized’. Digital Vitality When determining the digital vitality of language X, the scoresheet with the indicators has been used (shown in Appendix A). Language X has about 20.000 Wikipedia pages, it is somewhat used in social media, and its speakers are mildly digitally literate. Combined with the other indicators (not shown here, for brevity), they categorize language X as ‘Emergent’. Economic support Language X is not stigmatized, and its speakers are usually associated with middle class jobs, meaning there is strong economic support. School support Language X is used in about 70% of all the schools, and there is a large amount of material available to support education in language X. Therefore, there is strong school support. LVE score Like the digital vitality, the LVE score is determined using the scoresheet (Appendix A). Language X is spoken by a minority of the population, and mostly by the parental generation. It is used in home domains and in some new domains, written material and grammars exist. There is no explicit policy regarding language X, but many speakers support its maintenance. Calculating the LVE score based on aforementioned factors gives a score of 2.8, meaning that language X falls in the category ‘2.5-2.9’.

Figure 10. The proposed model applied to language X. In figure 10 all the scores for language X have been circled. For three factors language X scores a 4, meaning high support for NFLA-based programs, while for the other two factors language X scores a 3, meaning neutral support. Combined, these scores indicate that there is neutral to high support. From this, we can conclude that language X has enough support in place in order to implement NFLA-based revitalization programs. The model also shows that

specifically the amount of school support and the LVE-score can be improved, in order to create even more support. Language X is an example of a language that scored mostly positive on the model, but the opposite is also possible. For example, if language Y scored similar to language X on Digital Vitality and LVE score, but scored lower on the other factors, the applied model would look very different.

Figure 11. The proposed model applied to language Y. Figure 11 shows the model applied to language Y. In this case, there is not enough support for the implementation of NFLA-based revitalization programs, as language Y scores either neutral or negative, but does not score positive on any of the factors. For NFLA-based programs to be implemented, language Y would do well to improve on all factors, but specifically the amount of economic and school support.

5. Case Study: Irish

This section will apply the proposed model to the Irish language. This case study will show the model’s shortcomings, and how easily applicable it is. First, a historical overview will be given, showing the decline of the Irish language and the revitalization that has been undertaken up to this date. Then, the model will be applied, by gathering all necessary information and drawing conclusions about the support for the implementation of NFLA- based programs to revitalize the Irish language. The Irish language has been chosen for several reasons. First, the language has been studied extensively, meaning that all the data necessary for applying the model will be available. Second, the Irish language is a peculiar case in the field of language revitalization. While it has the status of a national language, it is still used only by a minority of Irish citizens. And third, while efforts to revitalize the language have been underway since the end of the 19th century, little progress has been made (Carnie, 1995). These factors make the Irish language the perfect language to use as a case study. 5.1. Historical Overview of the Irish language

5.1.1. Decline The Irish language was stable and had considerable status from about 500 A.D. until the 12th century. This started to change with the advent of French and English speakers, caused by the Norman invasions. At first, this change was small, as many of the migrants adapted to the Irish language and culture, but by the end of the 15th century proclamations were issued by Henry VIII that, both directly and indirectly, discouraged the use of the Irish language. In the 16th century came the acts of plantation, where land was taken from Irish landowners and given to British immigrants. At the start of the 17th century, while Irish was still the language of the majority of the population, English had replaced Irish as the language of Parliament. A century later, English had grown to be the language of regular use of half of the population, the half that had most of the power. Irish, on the other hand, had become the language of the poor and disadvantaged, and lacked any official support or recognition (Edwards, 2010). In the 19th century the use of Irish declined even further, through a number of different causes. First was the attitude towards the Irish language from some of Ireland’s most famous politicians. For example, Daniel O’Connell, who was called the Great Emancipator for his relentless campaigning for Catholic emancipation, reportedly called Irish ‘inferior to the English tongue as the medium of all modern communication’ (Daunt, as cited in Edwards, 2010). The fact that important Irish politicians apparently had little regard for their own language, is likely to have caused regular people to lose respect as well. A second factor was the Catholic clergy, which was quick to switch to using the English language, mainly because Irish was used by the Protestants. In their case, the argument was ‘saving souls was more important than saving Irish’ (Edwards, 2010, p108). The Catholic priests were heavily involved in the educational system, with primary schools often being managed by priests who pushed against using Irish in the educational context. The National School system, which was established in 1831, actively worked to exclude Irish, both in principle and in practice (Edwards, 2010). However, the event that caused the most harm to the Irish language in the 19th century was the famine. Occurring from 1845 – 1849, it is estimated that almost 1 million Irish died, and the same number of people emigrated, mostly to the U.S., U.K. and Australia. Within a decade, the Irish population had shrunk by 20%, and continued to shrink, albeit at a slower rate, until 1930. At this point, the Irish population was 4 million, compared to more than 8

million in 1841 (Fotheringham, Kelly, & Charlton, 2013). The famine mostly affected people from lower socio-economic backgrounds, whose diets consisted mostly of potatoes, the affected crop. As stated before, it was also this group that contained most of the Irish speakers. The famine was thus the direct cause of a massive decline in the number of Irish speakers. The 1841 census set the number of Irish speakers around 4 million, which is half the population (Ranelagh, as cited in Kennedy, 2015). The 1891 census however, only 50 years later, reported that only 23% of the population spoke Irish. While this number might have been affected by respondents’ suspicion of British census motives, it still shows a steep decline in number of Irish speakers (Edwards, 2010). This decline continued in the 20th century. In the 1926 Census, only 18% of the population in Ireland self-reported as Irish speakers. This census offers no further details on the proficiency of these speakers, or the frequency they used Irish. The Census also shows that many of these Irish-speaking communities worked in agriculture and fishing, indicating that Irish had become the language of lower socio-economic groups living in rural areas. In comparison, English became the language associated most with urban and professional classes. However, English was also still being spoken by large parts of the lower socio- economic classes. This removed any social or economic value the language might have had and provided little incentive for Irish speakers to use or maintain their language, or for non- speakers to learn it (O’Rourke, 2011). While most researchers and language activists place the blame for the decline of Irish entirely at the feet of the British colonizers, others are more nuanced. Hickey (2003) attributed the decline to the association of Irish with poverty, backhandedness and the famine. Bradley (2014) shares this opinion, stating: ‘The acquiescence of the population in the spread of English was probably a more salient factor than any government decree” (p538). He implies that both the British and the Irish population had low attitudes towards the Irish language, which caused the lack of interest in the decline of the language and explains why Irish speakers did not intervene earlier. However, to hold mainly the Irish responsible for the decline of their language, as both Hickey and Bradley do, seems unreasonable. Besides, the low attitudes and the association of Irish with poverty did not emerge out of nowhere, but rather were fostered by decades of growing importance of English. For this reason, it does not seem logical to attribute mostly the low attitudes of the Irish, but rather place equal blame on a combination of all the factors mentioned in the above section, language policy put in place by the British, lack of attitude from the Irish and of course the Famine. 5.1.2. Current situation The precise number of current speakers differ based on the source that is consulted. The latest version of Ethnologue estimates the total number of Irish speakers to be 1.200.300, of which 170.300 are L1 speakers (Eberhard, Simons & Fenning, 2019). However, the Central Statistics Office of Ireland reports 1,761,420 speakers in Ireland alone, based on the 2018 census. This census does not include data on the number of L1 speakers, but it does state that 111,473 people use Irish weekly while just 73,803 people use Irish daily (Central Statistics Office Ireland, 2018). In other words, while 39.8% of the population identifies themselves as capable of speaking Irish, only 1.6% of the population speaks Irish daily.

5.2 Revitalization

5.2.1. Early revitalization (1892 – 1999) Towards the end of the 19th century the rapid decline of the Irish language caused concern with its speakers. In 1892 the Gaelic League was established, which turned language revival into a political issue. The League emphasized Ireland’s unique cultural identity compared to England and saw this as the main reason as to why the Irish language should be restored. The League quickly gained popularity among the Irish population and it was essential in the establishing of language classes throughout the country and in changing the language of instruction in Gaeltacht (region where Irish is the main language) schools to Irish, but they had no real political power. By 1912 the League’s focus on language was overshadowed by the war for independence, but after this was established, in 1921, did Irish politicians finally have the power to control the destiny of the Irish language (Bradley, 2014). After independence, and the establishment of the Irish Free State (now the Republic of Ireland), it became clear that the continuing decline of the number of Irish speakers was mainly due to economic issues. An undeveloped economy and subsequent poverty caused out-migration, which weakened the state of the Irish language. This was counteracted by the new State, which was committed to restore Irish to its previous position of national language (Antonini, Corrigan & Wei, 2002). Over the next century, all Irish governments have attempted to expand the number of Irish speakers in two ways. In the English-speaking part of the country, the Galltacht, Irish was (attempted to be) reintroduced as the main language, while in the Irish speaking part, the Gaeltacht, Irish was preserved as the main language. It was believed that the introduction of Irish in the Galltacht could be achieved by establishing Irish as the main language of instruction in the educational system. By 1939, over 60% of schools taught completely or partly through Irish. However, since English was still the language used mostly in society, there was no communicative need for Irish. This caused schools to over time revert to English as their language of instruction, and by 1960 schools were actively discouraged from using Irish. Later, linguists argued that this plan to reintroduce Irish failed because it was only focused on the acquisition, but not on the actual use of the language (Bradley, 2014). In the Gaeltacht, efforts were focused on protecting and expanding communities with native speakers. Since these communities often lived in impoverished areas, the main threat against the Irish language was migration of native speakers to English speaking regions. Although government measures and economic investments have halted this migration, and increased the population of the Gaeltacht, the amount of native Irish speakers was still declining. Like in the Galltacht, the reason for this is widely assumed to be the lack of use of the Irish language outside of the educational system. Even in the Gaeltacht, the majority of families did not use Irish at home, which indicates that a lack of communicative need was again the main reason for the decline of the number of speakers (Bradley, 2014). 5.2.2. Revitalization 2000-2010 Abovementioned revitalization efforts only focused on the Republic of Ireland, as Northern Ireland was still under British rule. The Irish language did not have legal status, nor was there any policy that was specifically intended for the promotion or support of the language (Antonini, Corrigan & Wei, 2002). In April 1999, after a long period of struggle, the Belfast Agreement (also known as the Good Friday Agreement) was signed by the British and Irish governments, as well as by most of the political parties in Northern Ireland. The Agreement states how North-Ireland will be governed and discusses its status as part of the Union. On the topic of minority languages, the Agreement states:

“All participants recognise the importance of respect, understanding and tolerance in relation to linguistic diversity, including in Northern Ireland, the Irish language, Ulster- Scots and the languages of the various ethnic communities, all of which are part of the cultural wealth of the island of Ireland.” (Belfast Agreement (1998), p24). This meant that for the first time since the establishment of Northern Ireland, the government would legitimize and even support the Irish language (Antonini, Corrigan & Wei, 2002). The Agreement reports concrete actions that the British Government pledges to undertake, such as actively promoting both languages, facilitating their use in speech and writing, removing any restrictions that are in place to hamper their maintenance or development and encourage their use in education. A public institution was set up to carry out these tasks throughout the whole island of Ireland, so both the Republic of Ireland and Northern Ireland, called Foras na Gaeilge (the Gaelic Institution). The institution lists their functions as follows: “Promotion of the Irish language; facilitating and encouraging its use in speech and writing in public and private life in the South and, in the context of Part III of the European Charter for Regional or Minority Languages, in Northern Ireland where there is appropriate demand; advising both administrations, public bodies and other groups in the private and voluntary sectors; undertaking supportive projects, and grant-aiding bodies and groups as considered necessary; undertaking research, promotional campaigns, and public and media relations; developing terminology and dictionaries; supporting Irish-medium education and the teaching of Irish” (Foras na Gaeilge, 2019). Foras na Gaeilge does not take concrete actions themselves, but rather gives advice where needed, and offers financial support to those who request it. In 2013, a reorganization marked the start of a new phase in the development of the Irish language, where 19 Irish language organizations that were funded by Foras, were combined and divided into 6 Lead Organizations. These newly formed organizations focused on different areas of the Irish language, such as Irish-medium education, education in the English language sector, community and economic development, language awareness and representation, and the development of opportunities for the use of Irish and networks for both young people and old people (‘A partnership approach’, Foras na Gaeilge, 2019). These organizations focus on aspects that correspond with factors from the proposed model, such as economic support and school support. While it is unclear how this division was made, it is notable that together, these organizations focus on most, if not all, aspects of language revitalization. 5.2.3. Revitalization after 2010 In 2010, the Government of Ireland released their 20-year strategy plan for the Irish language. Their main objective is ‘to increase on an incremental basis the use and knowledge of Irish as a community language’ (20-year Strategy For The Irish Language 2010-2030, 2010, p3). In order to achieve this main objective, several smaller objectives were composed. Some of these smaller objectives, of which there are 13 in total, promise the upholding of several earlier laws, such as the Official Languages Act 2003, the Planning and Development Act 2000 and the Broadcasting Act 2001, which all serve to lend official support to the Irish language. Other objectives concern the role of Irish in the educational system, in the media and in the European Union. Each objective consists of several concrete measures that are being taken or will be taken. What is noticeable about these measures is that nearly all are focused on either formal or informal language acquisition, such as more support for using Irish in the classroom or for broadcasting Irish radio programs. The only measures that can be considered nonformal language acquisition, are measures towards creating material in Irish, such as books, that speakers can use to improve their proficiency. Classifying such measures as non-formal language acquisition is debatable, as that term implies a willingness

from the perspective of the speaker to improve their language. If the aim is to read more literature but not necessarily improve one’s language proficiency, it cannot be considered non-formal. Instead, it would be classified as informal language acquisition, as it is not the aim to improve, but this will nevertheless occur as a side effect, as reading will improve one’s vocabulary (Krashen, 1989). The fact that the official government strategy contains very little actions using nonformal language acquisition, shows that the government does not consider this type of language acquisition to be helpful towards increasing the use of the Irish language, or at least, they have not considered the possibility. Foras na Gaeilge itself does not offer any courses, but they do promote the use of Duolingo and the online courses developed by Gaelchultúr, another Irish language institution. This indicates a difference in policy. A possible explanation is that Foras na Gaeilge promotes these programs because they are digital programs, not because they use Non-formal language acquisition. This would indicate that neither the Government nor language institutions are aware of the concept of non-formal language acquisition, or are not aware of how it can be used in revitalization programs.

5.3 Application of the model As stated in section 4, the first step in using the model is to gather all the necessary data. That means determining the status of Irish on the Revitalization E-Gids, the score on the Digital Vitality Scale, the amount of economic support, the amount of school support and to calculate the average LVE-score. Status According to Ethnologue, the status of the Irish language is 6b – threatened (Eberhard, Simons & Fenning, 2019). This means that ‘The language is used orally by all generations but only some of the child-bearing generation are transmitting it to their children’ (Lewis & Simons, 2010). Scoring Irish on the Revitalization E-GIDS is complicated, as this scale assumes that at one point there was no intergenerational language transmission, and the different labels indicate how the language has succeeded in establishing this transmission. However, Irish has always been transmitted through generations, just at a decreasing scale. In category 6a all members of a third generation acquire the language, and it is the first language for all children, which is not the case for Irish. For this reason, the choice has been made to score Irish as ‘6b – re-established’. In this category, some members of a third generation of children are acquiring the language in the home, but not all of them, which is the case for the Irish language. Digital Vitality To determine the Digital Vitality of the Irish language, the extent to which the Irish language scored was assessed for each indicator: 1. Evidence of connectivity: According to the EU, 98% of the population in Ireland has internet access (DESI 2019). This indicates that there is digital connectivity, so for this factor Irish scores 2. 2. Digital literacy: 49% of the population reports having basic digital skills, while 29% report having above basic digital skills (DESI 2019), i.e. speakers of the language are mildly digitally literate, so score 3-4. 3. Internet penetration: only 16% of the population reports never using the internet, meaning 84% of the population has used the internet over the past 12 months. This coincides with score 6.

4. Character encoding and input/output methods: the Irish language has a standardizes character/script encoding (‘Unicode – Languages and Scripts’, 11-7-2019). Therefore, it scores 6 on this factor. 5. Availability of language resources: several of the intermediate language resources are available (Virtual Language Observatory, 11-7-2019). Irish scores 5 on this factor. 6. Use for e-communication: this factor is tricky to determine, as it refers mainly to private communication. However, taking into consideration that 98% of the population has access to the Internet (DESI 2019), and that 39,8% of the population is capable of speaking Irish and does so regularly (Central Statistics Office Ireland, 2018), it can be assumed that Irish is used in e-communication regularly, scoring a 5. 7. Use on social media: Irish is used on Twitter (https://twitter.com/BBCSpors, 11-7- 2019), Instagram (https://www.instagram.com/explore/tags/gaelic/, 11-7-2019), Tumblr (https://www.tumblr.com/search/%23gaeilge, 11-7-2019), Facebook (https://www.facebook.com/groups/166677873392308/, 11-7-2019), and likely other social media that the author has no knowledge of. This means that Irish scores 6 on this factor. 8. Availability of Internet Media: there are websites, e-books, television programs, radio programs, YouTube videos, blogs and likely more media available in Irish. Therefore, it scores 6. 9. Wikipedia: Currently, there are 51,179 Wikipedia articles in Irish (‘List of Wikipedias’, 11-7-2019). This means that a score of 4 has been assigned. 10. Available Internet Services: there are online news(papers) available, as well as search engines, advertising and customer service. No evidence was found of the availability of other Internet services, although it is likely that some exist, like video subtitles. Irish scores 4 on this factor. 11. Localized social network: of the major social networks, only Facebook and Twitter were available in Irish, therefore scoring a 5 on this factor. 12. Localized software: Microsoft Office, Mozilla Firefox, Mozilla Thunderbird and OpenOffice are available in Irish, as well as three video games, and Samsung has released a mobile phone specifically for the Irish market. No Apple products are available in Irish, which is why Irish scores a 4 on this factor. 13. Machine translation tools/services: Irish is available on Google Translate, in a large number of language pairs. However, there is no other (widely used) machine translation tool that offers Irish. Therefore, Irish scores a 5. 14. Dedicated internet top-level domain: there is no dedicated Internet top-level domain, so Irish scores a 3 on this factor. For clarity, all the scores are presented in the table below, alongside the maximum possible score for that specific indicator.

Indicator Score Maximum score 1 Evidence of connectivity 2 2 2. Digital literacy 3-4 6 3. Internet penetration 6 6 4. Character encoding 6 6 5. Availability of language resources 5 6 6. Use for e-communication 5 6 7. Use on social media 6 6 8. Availability of internet media 6 6 9. Wikipedia 4 6 10. Available Internet services 4 6 11. Localized social networks 5 6 12. Localized software 4 6 13. Machine translation tools/services 5 6 14. Dedicated internet top-level domain 3 5 Figure 12. Assessment of the Digital Vitality of the Irish language. It can be concluded from figure 12 that Irish scores an average between 4 and 5, which corresponds to either Developing or Vital. Looking at the descriptions of these levels, it becomes clear that Irish falls somewhere in between these levels. It is frequently used in communication and social media, and a variety of digital services is available, including a machine translation tool, which corresponds to the status of Vital. However, Wikipedia projects are not big or actively used, nor are most used operating systems and general- purpose software localized in the language. For this reason, the choice has been made to label Irish as Developing instead of Vital. Economic Support The 2011 Census showed that of those who spoke Irish daily, 49% had received a third level degree or higher, compared to only 28,5% of those who did not speak Irish daily (Central Statistics Office, 2011). In other words, daily Irish speakers were higher educated than non- daily speakers. However, the top occupations of the daily speakers were (primary) teachers, farmers, administrative assistants, retail workers and nurses, which are not generally considered to be occupations for which a higher education is needed (Central Statistics Office, 2011). People’s attitude towards Irish is generally positive, as shown by Antonini e.a. (2002). Respondents in this survey reported feeling positive about the Irish language although they thought more could and should be done to revitalize the language. 93% of respondents also stated that the Irish Government should improve employment opportunities for Irish speakers (Antonini e.a., 2002). Summarized, Irish is not stigmatized, and its speakers are high-educated, but there are not enough employment opportunities and speakers usually have menial jobs. It is for these reasons that the language has been classified as having ‘Considerable Support’. School Support According to the Irish Government, it has been their policy that Irish is taught in all schools, in both the English and Irish speaking regions, from Primary to Leaving Certificate level, and their aim is to continue (Government of Ireland, 2010). According to the proposed model, Irish thus scores ‘Complete support’ on this factor.

LVE-Score The LVE-score is determined in a similar manner as the Digital Language Vitality Score, using the scoresheet. 1. Intergenerational language transmission: there is an unbroken chain of intergenerational language transmission, as there are still parents who transfer Irish to their children. The language is used in all domains by at least some part of the population, and all children use the language in at least one domain, namely education. Therefore, the score is 4. 2. Absolute number of speakers: as stated in section 5.1, the current number of speakers is reported as 1,761,420 by the Central Statistics Office (2011). However, this factor is not awarded a score. 3. Proportion of speakers within the total population: 39.8% of the Irish population speaks Irish (Central Statistics Office, 2011), meaning the language falls under score 2. 4. Loss of existing language domains: while English is the dominant language, Irish is still used in a number of domains, especially in the Irish-speaking regions. For this factor, the score is 4. 5. Response to new domains (including media): as discussed when determining the Digital Language Vitality Score, Irish is used in most social media networks, as well as in radio and television. It is used in most new domains, indicating score 4. 6. Materials available for purposes of education and literacy: written material exists, and students are taught literacy in Irish. Under the 2003 Official Languages Act, the Government pledges to provide all public service in Irish, alongside English (Government of Ireland, n.d.). In other words, the language is used in administration and education, thereby complying with the requirements of score 5. 7. Official language attitudes and policies: the Irish language is protected, under the aforementioned Official Languages Act. Since 2007, Irish is even recognized by the EU as an official language, gaining even more protection (https://europa.eu/european-union/about-eu/eu-languages_en, 11-7-2019). For this reason, Irish scores 5 on this factor. 8. Speakers’ own attitudes and policies: according to the survey by Antonini e.a. (2002), the majority of Irish speakers supports language maintenance. However, there are some conflicting opinions. Most of the respondents agreed that the Government should do more to maintain the Irish language, including increasing funding for the teaching of Irish in schools, while a significant percentage thinks the promotion of Irish has been successful. Despite this difference, all Irish speakers generally value their language, which is why it scores 5 on this factor. 9. Amount and quality of relevant documentation: there are several Irish grammars and (online) dictionaries. New literature is published, and news media are updated daily. There are radio and television programs available. For these reasons, Irish scored 5 on this factor.

For clarity, the factors and scores have been presented in figure 13.

Factor Score 1. Inter-generational Language Transmission 4 2. Number of speakers - 3. Proportion of speakers 2 4. Loss of existing language domains 4 5. Response to new domains (including media) 4 6. Materials available for purposes of education and literacy 5 7. Official language attitudes and policies 5 8. Speakers’ own attitudes and policies 5 9. Amount and quality of relevant documentation 5 Figure 13. Assessment of the LVE-factors. Calculating the average of these scores, gives a final score of 4.25. Conclusion

Figure 14. The proposed model applied to the Irish language. Figure 14 shows the proposed model, with the Irish scores circled. For two of the five factors, the score is 3, meaning neutral support for NFLA-based programs, according to figure 8. The other three factors are scored in category 5, meaning very high support. The average score of all five factors is 4.2, meaning this language falls in category 4. From applying the proposed model to the Irish language, we can conclude that there is high support for the implementation of NFLA-based revitalization programs. While nothing can be predicted about the success of any possible NFLA-program, there is enough support for these type of programs to be implemented. As stated before, the proposed model can also give insight into areas which can be improved, in order to create more support for the implementation of NFLA-based programs. In this case, only two factors score lower than 4, namely Digital Vitality and Economic Support. Irish scores quite low on these factors, compared to the other three. Assuming the Irish government wants the revitalization of Irish to be successful, they would do well to focus on improving these factors. Improving the Digital Vitality of the language seems especially important, as many NFLA-based programs rely on digital means.

6. Discussion

In this section, the proposed model and its application in the case study will be discussed, in order to provide an answer to the research question as stated in section 1. Any limitations to this study will be discussed, as well as possibilities for further research. To end this study, several concluding remarks will be given. 6.1. Discussion of the model In order to answer the research question, this work aimed to create a model that could be used to test the support for non-formal language acquisition-based programs to be used to revitalize a threatened language. Four of the most well-known and relevant frameworks were discussed, and factors from these frameworks were used, among the author’s own factors, to create the proposed model. The model was then applied in a case study of the Irish language, concluding that there was ample support for the implementation of NFLA-based programs. After applying the model, several shortcomings have become apparent. A major issue concerns the Digital Language Vitality Scale. While it uses a variety of indicators, thereby giving an accurate representation of the Digital Vitality of a language, the indicators themselves are not very clear and assume a significant amount of background knowledge from its users. For example, one needs to know what ‘dedicated Internet top-level domains’ are, before one is able to assign a score to this indicator. Without this knowledge, it is quite difficult to use this scale. Second, for many indicators the Scale provides ways of accessing the information needed to assign a score. For example, the address of a website that lists the amount of Wikipedia pages per language. However, this is not the case for all indicators. For several indicators, especially those relating to Digital Performance, the article gives no way of accessing the necessary information. Instead, it describes software that would be useful, but is not available. This will inevitably result in that every individual that uses this scale, accesses the necessary information in a different way, which is detrimental for the validity of the Scale. After all, if different people use different information to score the same indicator, the scores cannot be compared. Third, the Scale provides no way of determining the final score after all the indicators have been scored. Calculating an average score is not useful, as not all indicators use the same evaluational scales. The only option is for the Scale’s user to collect the scores from all the indicators, and judge which of the 6 levels corresponds best, which is not objective. Lastly, the 6 levels of the scales could be improved. The difference between level 1 and level 2 is quite small: both levels state that the language can currently not be used digitally, but at level 2 it could be used if the right technological support were available. The difference between level 4 and 5 on the other hand, is quite large, which caused an issue when determining what level the Irish language would fall on. Level 5 was too high, but level 4 was too low. If there would be less difference between these levels, this would not be an issue, as the levels were more nuanced and it would be easier to assign a level to a language. Other shortcomings concerned the two factors created by the author, School Support and Economic Support. When assigning a score to the level of school support for the Irish language, the main difficulty was that although some Irish education was compulsory, this did not automatically ensure that the education was of sufficient quality. In fact, inspectors from the Government reported that only half of the primary schools taught Irish at a high enough level (Government of Ireland, 2010). While the report does not specify what exactly constitutes ‘quality education’, it clearly states that the current level of Irish education is not sufficient. However, the proposed model only looks at the amount of education, not at the quality, which is something that should be taken into consideration.

In regard to the factor Economic Support, after applying the model it was clear that the criteria developed in the model were not sufficient to assign a score, as they are still very vague. Instead, clear criteria need to be developed, such as the percentage of speakers with a higher education, and the percentage of speakers in different levels of socio-economic status. While the LVE in itself is a good framework, several factors overlap with the Digital Language Vitality Scale (DLVS). For example, the LVE factor ‘responses to new domains’ largely coincide with the factors ‘use for e-communication’ and ‘use on social media’ from the DLVS. When revising the model, this needs to be altered and the overlapping factors need to be removed. During the application of the model, several good qualities were revealed as well. The model uses a variety of frameworks and factors and is therefore quite well-rounded and takes into account (most of) the important features of a language. Working with a 5-point evaluational scale means it is nuanced enough, without being overly nuanced. Finally, the model is fairly easy to use, and is therefore also suitable for use by language activists, who might not have a background in linguistics. 6.2. Case study In section 5 of this study, the model was applied in a case study on the Irish language. First, background information was provided on the decline of the language, as well as on previous efforts to revitalize the Irish language. Then, the model was applied, and conclusions were drawn about the support for the implementation of NFLA-based programs. The background information provided was extensive, and some parts may have seemed irrelevant. However, historical context is important in all manners of language revitalization. If it is unclear how a language became extinct, how will one know where to focus revitalization efforts? For this reason, the choice was made to include extensive information. 6.3. Research Question As stated in section 1, the research question this work aimed to answer was: in what cases can non-formal language acquisition be used in language revitalization? After creating, applying and critically discussing the model, the conclusion can be drawn that there are several factors that can be used to decide if there is enough support to implement NFLA-based programs, namely the language’s status, its digital vitality, the amount of economic and school support and the score on the UNESCO LVE scale. Taken together, these factors can indicate whether a language has progressed enough to start creating NFLA-based programs, or whether measures need to be taken to improve the language before starting these programs. However, the model is not complete yet. As mentioned above, several factors and their scores need to be revised, in order to make the model more suitable for usage. 6.4. Limitations of the study As with any study, there are limitations to the present study. The main limitation is that, due to the scope of this work, only four relevant frameworks have been discussed. It would be better to discuss more frameworks, in a manner similar to Edwards when creating his typology of minority language settings (2010). That would make the model more theoretically grounded, and hopefully broader applicable, as ideally, the model would be applicable to all languages. While using Irish as a case study was a logical choice, as explained in section 5, it would be good to apply the model to more languages, as that is likely to reveal other shortcomings or factors that should be included. Preferable, it would be applied to non- Western languages, to remove any bias that the model is likely to have. It would be good to select a number of languages from different continents, and with different statuses, to truly test the applicability of the proposed model.

6.5. Further research Once the present model has been improved and tested on a number of different languages, further research could focus on developing a similar model, that focuses on the next question: what factors are necessary for NFLA-based programs to succeed in (helping) language revitalization? This model will focus more on the program itself, both the form and the content, and take into account factors like the type of speaker the program is aimed at. Together, these two models will first determine if NFLA-based programs can be implemented and then how they should be implemented. Hopefully, this will provide language activists and policy-makers with concrete steps they can undertake to use NFLA-based programs in the revitalization of their language. With time, NFLA-based programs could even become standard operating procedure in halting the decline of a language. If NFLA-based programs appear to be unsuccessful, models can be developed that test the support or the success of different types of revitalization programs. 6.6. Concluding remarks While it would be spectacular to develop a model that can be used to predict the success of a language revitalization program, unfortunately that is nearly impossible. Instead, this study aimed to provide a very small part of such a model, by focusing on possibility of implementing a very specific type of program, namely NFLA-based. It is the hope that similar models will be developed, focusing on a different aspect of the process of language revitalization, or on a different type of program. Together, these could then be combined, to form one cohesive, comprehensive model that can predict the success of revitalization programs. Let us hope that such a model will be developed in the near future, before all endangered languages have become extinct, and the concept of language revitalization has become obsolete.

7. References

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British Geographers, 38(2), 221-237. Retrieved from http://www.jstor.org.proxy.uba.uva.nl:2048/stable/24582470 Gibson, M. (2015). A Framework for Measuring the Presence of Minority Languages in Cyberspace. In Proceedings of the 5th SALT-MIL Workshop on Minority Languages “Strategies for developing Machine Translation for minority languages”, p1-6. Gobbo, F. (2019). Lecture Notes in Multilingualism in a European Perspective. Amsterdam: University of Amsterdam. Unpublished textbook. Version date: February 27, 2019. Government of Ireland. (n.d.). Official Languages Act 2003. Retrieved July 11, 2019, from https://www.dccae.gov.ie/en-ie/about-us/compliance/official-languages-act/official- languages-act/Pages/default.aspx Government of Ireland. (2010). 20-year strategy for the Irish Language 2010-2030. Retrieved from https://www.chg.gov.ie/app/uploads/2015/07/20-Year-Strategy-English- version.pdf Grenoble, L., & Whaley, L. (2006). Saving languages : an introduction to language revitalization. Cambridge: Cambridge University Press. Hickey, R. (2011). The languages of Ireland: an integrated view. In Hickey, R. (eds) Researching the languages of Ireland. Uppsala: Uppsala University. Kennedy, I. (2015, 9 maart). The Decline of the Irish Language in the Nineteenth Century. - The Yeats Society. Geraadpleegd op 10 mei 2019, van https://www.yeatssociety.com/news/2015/03/09/the-decline-of-the-irish-language-in-the- nineteenth-century/ Kornai, A. (2013). Digital language death. PLoS ONE, 8(10). Krashen, S. (1989). We Acquire Vocabulary and Spelling by Reading: Additional Evidence for the Input Hypothesis. The Modern Language Journal, 73(4), 440–464. https://doi.org/10.1111/j.1540-4781.1989.tb05325.x Lewis, M., & Simons, G.F. (2010). Assessing Endangerment: Expanding Fishman's GIDS. Revue Roumaine de Linguistique, 55(2), 103–120. https://doi.org/10.1017/cbo9780511783364.003 McGivney, V. (1999) Informal learning in the community: a trigger for change and development. Leicester: National Institute of Adult Continuing Education (England and Wales). MIME. (2018). MIME Vademecum. Retrieved from https://www.mime- project.org/vademecum/ Mok, O. N. A. (2011). Non-formal learning: clarification of the concept and its application in music learning. Australian Journal of Music Education, 1, 11–15. O’Rourke, B. (2011). Galician and Irish in the European context : attitudes towards weak and strong minority languages. Basingstoke: Palgrave Macmillan. Paulston, C. B. (1998). Linguistic Minorities in Central and East Europe: an Introduction. In C. B. Paulston & D. Peckham (Eds.), Linguistic Minorities in Central and East Europe. Clevedon: Multilingual Matters.

Pimento, D., Prado, D., & Blanco, A. (2019). Twelve years of measuring linguistic diversity in the Internet: balance and perspectives. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000187016 Project | The Digital Language Diversity Project. (n.d.). Retrieved March 8, 2019, from http://www.dldp.eu/en/content/project Scribner, S., & Cole, M. (1973). Cognitive Consequences of Formal and Informal Education. Science, 182(4112), 553-559. Retrieved from http://www.jstor.org/stable/1737765 Soria, C., Quochi, V., Russo, I., Gurrutxaga, A., & Ceberio, K. (2017). A Digital Language Vitality Scale and Indicators. Digital Language Diversity Project. The Unicode® Standard: A Technical Introduction. (2018, January 29). Retrieved March 8, 2019, from https://www.unicode.org/standard/principles.html. UNESCO. (2003). Language Vitality and Endangerment. Retrieved from http://www.unesco.org/new/fileadmin/MULTIMEDIA/HQ/CLT/pdf/Language_vitality_an d_endangerment_EN.pdf

8. Webography

Name Company/ URL Date consulted Webpage

Foras na Gaeilge https://www.forasnagaeilge.ie/about/about- 23-06-2019 foras-na-gaeilge/?lang=en

BBC starts Pidgin https://www.bbc.com/news/world-africa- 15-03-2019 digital service for West 40975399 Africa audiences

Busuu https://www.busuu.com/ 10-03-2019

Digital Language http://www.dldp.eu/ 03-03-2019 Diversity Project

DuoLingo https://www.duolingo.com/ 10-03-2019 12-03-2019

Google Translate https://translate.google.com/ 15-03-2019

Memrise https://www.memrise.com/ 10-03-2019

Mobility and Inclusion https://www.mime-project.org/ 04-03-2019 in Multilingual Europe

Rosetta Stone https://www.rosettastone.com/ 10-03-2019

Eurostat – level of http://appsso.eurostat.ec.europa.eu/nui/ 11-7-2019 internet access show.do?dataset=isoc_ci_in_h&lang=en

Unicode – Languages https://unicode.org/cldr/charts/latest/ 11-7-2019 and Scripts supplemental/languages_and_scripts.html

Virtual Language https://vlo.clarin.eu/?0&fq=language 11-7-2019 Observatory Code:code:gle&fqType=languageCode:or

List of Wikipedias https://en.wikipedia.org/wiki/List_of_ 11-7-2019 Wikipedias#Detailed_list

EU The Digital https://ec.europa.eu/digital-single- 11-7-2019 Economy and Society market/en/desi Index

9. Appendix A - Scoresheets

9.1. LVE scoresheet

Score Factor 0 1 2 3 4 5 1. Inter- There The language The language is The language The language is The language generational exists no is used mostly used mostly by is used mostly used by some is used by all Language speaker. by very few the by the children in all ages, from Transmission speakers, of grandparental parental domains; it is children up. great- generation and generation used by all grandparental up. and up. children in generation. limited domains. 2. Number of ------speakers 3. Proportion of None Very few A minority A majority Nearly all All speak the speakers speak the speak the speak the speak the speak the language language language language language language 4. Loss of existing The The language The language is The language Two or more The language language language is is used only in used in limited is used in languages may is used in all domains not used in a very social domains home domains be used in most domains and any restricted and for several and for many social domains for all domain domains and functions functions, but and for most functions and for any for very few the dominant functions function functions language begins to penetrate even home domains 5. Response to The The language The language is The language The language is The language new domains language is is used in only used in some is used in used in most is used in all (including not used in a few new new domains many domains new domains new domains media) any new domains domains 6. Materials There is no A practical Written Written Written There is an available for ortography orthography materials exist, materials exist materials exist, established purposes of available to is known to but they may and children and at school, orthography, education and the the only be useful may be children are literacy literacy community community for some exposed to the developing tradition with and some members of written form literacy in grammars, material is the community; at school. the language. dictionaries, being written. and for others, Literacy is not Writing in the texts, they may have a promoted language is not literature, and symbolic through print used in everyday significance. media. administration. media. Writing Literacy in the education in the language is language is not used in a part of the administration school and education. curriculum.

7. Official Minority The dominant Government No explicit Minority All languages language languages language is encourages policy exists languages are are protected attitudes and are the sole assimilation to for minority protected policies prohibited official the dominant languages, the primarily as the language, language. There dominant language of the while non- is no protection language private dominant for minority prevails in the domains. The languages are languages public domain use of the neither language is recognized prestigious nor protected 8. Speakers’ own No one Only a few Some members Many Most members All members attitudes and cares if the members support members support value their policies language is support language support language language and lost; all language maintenance; language maintenance wish to see it prefer to maintenance; others are maintenance; promoted use a others are indifferent or other are dominant indifferent or may even indifferent or language may even support may even support language loss support language loss language loss 9. Amount and No Only a few There are some There may be There are one There are quality of material grammatical grammatical an adequate good grammar comprehensive relevant exists sketches, sketches, word- grammar or and a number grammars and documentation short lists, sufficient of dictionaries, wordlists, and and texts useful amount of adequate extensive fragmentary for limited grammars, grammars, texts; constant texts. Audio linguistic dictionaries, dictionaries, flow of and video research but and texts, but texts, language recordings do with inadequate no everyday literature, materials. not exist, are coverage. Audio media; audio and Abundant of unusable and video and video occasionally annotated quality, recordings may recordings updated high-quality or are exist in varying may exist in everyday audio and completely quality, with or varying quality media; video un-annotated. without any or degree of adequate recordings annotation. annotation. annotated exist. high-quality audio and video recordings.

9.2. Digital Language Vitality scoresheet

1 2 3 4 5 6 1 Connectivi There is There is ty no positive evidence evidence of of connective- connecti- ty vity 2 Digital Speakers Speakers of Speakers of Speakers of Speakers of literacy of the the the the the language language language language language are highly are are are mildly are mildly digitally digitally minimally digitally digitally literate illiterate digitally literate literate literate 3 Internet Between Between 5% Between Between Between Over 70% penetra- 0% and and 20% 20% and 30% and 50% and tion and 5% 30% 50% 70% digital population size 4 Character/ Language Language Language Language Language Language script with no with no for which a with a with a with a encoding standar- standar- script consistent standardized standardized dized dized script proposal is and agreed character/ character/ script encoding; available upon script script encoding alternative encoding encoding; encoding; and no supported that may fonts, fonts, alterna- script is not have keyboards keyboards tive script used entered and software and software is used already the may not be are updated standardisa- fully and tion process available available 5 Availibility No LRs e-dictionary At least two Basic LRs Most of the Most of the of available (bilingual or basic LRs and, at intermediate advanced language in digital monolingu- least, three LRs LRs resources format al) intermediat e LR’s 6 Use for e- No use for At least one At least two More than More than communic e- communica- communica- two two ation communication tion communica- communica- tion medium mediums tion tion media that is used that are mediums that are used at least used at least that are used everyday rarely occasionally at least regularly 7 Use on No use on One or two At least At least More than social social media social media three social three social three social media that are media that media, one media used used at least are used at of which is everyday or rarely least used at least at least regularly everyday regularly 8 Availibility No digital Some digital Some digital A A wide of Internet media media media considerable variety of Media available in available in available in variety of digital the the the digital media is language language language media is available available

9 Wikipedia No Less than Between Between Between Over Wikipedia 100 articles 100 and 10,000 and 100,000 and 1,000,000 10000 100,000 1000,000 articles 10 Available No digital Some digital A number of A A wide internet services services digital considerable variety of services available in available in services variety of digital the the available in digital services language language the services available in language available in the language the language 11 Localized No social At least one Some social Many social social media social media media media networks localized in interface interface interface the localized localized localized language 12 Localized Neither At least one At least one Most used Main software operating operating desktop and operating operating system nor system one mobile systems and systems and general (either operating general application purpose desktop or system purpose software software mobile, (open or software localized localized in open or commercial) localized, the commercial) + some some language localized general specific purpose purpose software application localized software 13 Machine No machine At least one At least one More than translation translation (online) (online) one (online) tools/ser- for the service/tool, service/tool, service/tool, vices language at least one at least two more than 5 language language language pair or one pairs in both pairs direction directions 14 Dedicated No There is a Internet dedicated dedicated top-level Internet Internet top- domain top-level level domain domain