DOCSLIB.ORG

UC Berkeley UC Berkeley Electronic Theses and Dissertations

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

UC Berkeley UC Berkeley Electronic Theses and Dissertations Title robots.txt: An Ethnographic Investigation of Automated Software Agents in User-Generated Content Platforms Permalink https://escholarship.org/uc/item/1js7224r Author Geiger II, Richard Stuart Publication Date 2015 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California robots.txt: An Ethnographic Investigation of Automated Software Agents in User-Generated Content Platforms By Richard Stuart Geiger II A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy In Information Management and Systems and the Designated Emphasis in New Media in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Jenna Burrell, Chair Professor Coye Cheshire Professor Paul Duguid Professor Eric Paulos Fall 2015 robots.txt: An Ethnographic Investigation of Automated Software Agents in User-Generated Content Platforms © 2015 by Richard Stuart Geiger II Freely licensed under the Creative Commons Attribution-ShareAlike 4.0 License (License text at https://creativecommons.org/licenses/by-sa/4.0/) Abstract robots.txt: An Ethnographic Investigation of Automated Software Agents in User-Generated Content Platforms by Richard Stuart Geiger II Doctor of Philosophy in Information Management and Systems with a Designated Emphasis in New Media University of California, Berkeley Professor Jenna Burrell, Chair This dissertation investigates the roles of automated software agents in two user-generated content platforms: Wikipedia and Twitter. I analyze ‘bots’ as an emergent form of sociotechnical governance, raising many issues about how code intersects with community. My research took an ethnographic approach to understanding how participation and governance operates in these two sites, including participant-observation in everyday use of the sites and in developing ‘bots’ that were delegated work. I also took a historical and case studies approach, exploring the development of bots in Wikipedia and Twitter. This dissertation represents an approach I term algorithms-in-the-making, which extends the lessons of scholars in the field of science and technology studies to this novel domain. Instead of just focusing on the impacts and effects of automated software agents, I look at how they are designed, developed, and deployed – much in the same way that ethnographers and historians of science tackle the construction of scientific facts. In this view, algorithmic agents come on the scene as ways for people to individually and collectively articulate the kind of governance structures they want to see in these platforms. Each bot that is delegated governance work stands in for a wide set of assumptions and practices about what Wikipedia or Twitter is and how it ought to operate. I argue that these bots are most important for the activities of collective sensemaking that they facilitate, as developers and non-developers work to articulate a common understanding of what kind of work they want a bot to do. Ultimately, these cases have strong implications and lessons for those who are increasingly concerned with ‘the politics of algorithms,’ as they touch on issues of gatekeeping, socialization, governance, and the construction of community through algorithmic agents. 1 Acknowledgements There are too many people for me to thank in completing this dissertation and my Ph.D. This work is dedicated my personal infrastructure of knowing and knowledge production – the vast assemblage of individuals who I have relied on for guidance and support throughout my time as a graduate student. There could be a whole dissertation of material documenting all the people who have helped me get to this point, but these few pages will have to suffice for now. The School of Information at UC-Berkeley has given me a wealth of resources to help me get to this moment, but the people have been the greatest part of that. My fellow members of my Ph.D cohort, Galen Panger and Meena Natarajan, have been on the same path with me for years, helping each other through all the twists and turns of this journey. I also want to thank many members of my Ph.D program, who have helped me refine and shape my ideas and myself. I’ve presented much of the work in this dissertation at both formal colloquia and informal talks over drinks in the Ph.D lounge, so thanks to Andy Brooks, Ashwin Matthew, Christo Sims, Dan Perkel, Daniela Rosner, Elaine Sedenberg, Elizabeth Goodman, Ishita Ghosh, Jen King, Megan Finn, Nick Doty, Nick Merrill, Noura Howell, Richmond Wong, Sarah Van Wart, and Sebastian Benthall for their ideas and support. The School of Information is also home to three members of my committee, without whom this dissertation could not have been written. I want to thank my chair and advisor Jenna Burrell for her time and energy in supporting me as a Ph.D student, as well as Paul Duguid and Coye Cheshire, for their years of service and support. They have helped me not only with research for this dissertation, but also in various other aspects of becoming an academic. I’d also like to thank Eric Paulos, my outside committee member, for his advising around my dissertation. And there are other professors at Berkeley who have helped shape this work and my thinking through classes and conversations: Greg Niemeyer, Ken Goldberg, Abigail De Kosnik, Cathryn Carson, Massimo Mazzotti, and Geoffrey Nunberg. I’d also like to thank South Hall administrative staff for all of their work in getting me to this point, including: Meg St. John, Lety Sanchez, Roberta Epstein, Siu Yung Wong, Alexis Scott, and Daniel Sebescen, as well as Lara Wolfe at the Berkeley Center for New Media and Stacey Dornton at the Berkeley Institute for Data Science. I’ve had the opportunity to collaborate with amazing researchers, who have shaped and expanded my thought. Aaron Halfaker, my long-time collaborator, has been a constant source of support and inspiration, and I know that we have both expanded our ways of thinking in our collective work over the years. I also want to thank Heather Ford, my fellow ethnographer of Wikipedia, for her collaboration, friendship, and support. I’m thankful to met the members of the Wikimedia Summer of Research 2011, who early on in my Ph.D helped me understand the full breath of Wikipedia research: Fabian Kaelin, Giovanni Luca Ciampaglia, Jonathan Morgan, Melanie Kill, Shawn Walker, and Yusuke Matsubara. I also want to thank many people at the Wikimedia Foundation, who have been incredibly helpful throughout this research including Dario Taraborelli, Diederik van Liere, Oliver Keyes, Maryana Pinchuk, Siko Bouterse, Steven i Walling, and Zack Exley. I’d also like to thank the Wikipedians and Twitter blockbot developers who have assisted with this research, both in the time they took to talk with me and in the incredibly important work that they do in these spaces. I am also grateful to have an abundant wealth of friends and colleagues in academia, who have helped shape both me and my work. Many of these people I met when we were both graduate students, although some are now faculty! I’ve met many of these people at conferences where I’ve presented my work or heard them present their work, and our conversations have helped me get to the place where I am today. These people include: Aaron Shaw, Airi Lampinen, Alex Leavitt, Alyson Young, Amanda Menking, Amelia Acker, Amy Johnson, Andrea Wiggins, Andrew Schrock, Anne Helmond, Brian Keegan, Caroline Jack, Casey Fiesler, Charlotte Cabasse, Chris Peterson, Dave Lester, Heather Ford, Jed Brubaker, Jen Schradie, Jodi Schneider, Jonathan Morgan, Jordan Kraemer, Kate Miltner, Katie Derthick, Kevin Driscoll, Lana Swartz, Lilly Irani, Mako Hill, Matt Burton, Melanie Kill, Meryl Alper, Molly Sauter, Nate Tkacz, Nathan Matias, Nick Seaver, Norah Abokhodair, Paloma Checa-Gismero, Rachel Magee, Ryan Milner, Sam Wolley, Shawn Walker, Stacy Blasiola, Stephanie Steinhardt, Tanja Aitamurto, Tim Highfield, Whitney Philips, and Whitney Erin Boesel. I’m sure I’m also leaving some people out of this long list, but this only goes to show you that so much of what I have accomplished is because of all the people with whom I’ve been able to trade ideas and insights. There have also been many faculty members who have generously helped me think about my work and my career, as well as given me many opportunities during my time as a graduate student. I want to particularly thank David Ribes, my advisor in my M.A. program at Georgetown, for helping me first dive into these academic fields, and for continuing to help support me. I also want to thank those scholars who have helped me think through my work and my career in academia: Amy Bruckman, Andrea Tapia, Andrea Forte, Andrew Lih, Annette Markham, Chris Kelty, Cliff Lampe, David McDonald, Geert Lovink, Gina Neff, Janet Vertesi, Kate Crawford, Katie Shilton, Loren Terveen, Malte Ziewitz, Mark Graham, Mary Gray, Nancy Baym, Nicole Elison, Paul Dourish, Phil Howard, Sean Goggins, Steve Jackson, Steve Sawyer, T.L. Taylor, Tarleton Gillespie, Zeynep Tufecki, and Zizi Papacharissi. Institutionally, I’d like to thank many departments, centers, and institutes for supporting me and my work, both at Berkeley and beyond. Locally, I’m thankful for the support of the UC- Berkeley School of Information, the Department of Sociology, the Center for Science, Technology, Medicine, and Society (CSTMS), the Center for New Media, and the Berkeley Institute for Data Science. Further out, I’m thankful for the support of the Center for Media, Data, and Society at Central European University, the Oxford Internet Institute, the MIT Center for Civic Media, the Berkman Center at Harvard Law School, the Consortium for the Science of the Sociotechnical, and the Wikimedia Foundation. People and programs at these institutions have graciously hosted me and other like-minded academics throughout the years, giving an invaluable opportunity for me to grow as a scholar.
Recommended publications
  • Sociotechnical Systems and Ethics in the Large

    Sociotechnical Systems and Ethics in the Large

    Sociotechnical Systems and Ethics in the Large Amit K. Chopra Munindar P. Singh Lancaster University North Carolina State University Lancaster LA1 4WA, UK Raleigh, NC 27695-8206, USA [email protected] [email protected] Abstract question has inspired various versions of “do no harm to hu- mans” maxims, from Asimov to Bostrom and Yudkowsky Advances in AI techniques and computing platforms have (2014). And, partly this interest stems from imagining that triggered a lively and expanding discourse on ethical decision-making by autonomous agents. Much recent work agents are deliberative entities who will make choices much in AI concentrates on the challenges of moral decision mak- in the same way humans do: faced with a situation that de- ing from a decision-theoretic perspective, and especially the mands deliberation, an agent will line up its choices and representation of various ethical dilemmas. Such approaches make the best one that is also the most ethical. The trol- may be useful but in general are not productive because moral ley problem, a moral dilemma that has been the subject of decision making is as context-driven as other forms of deci- extensive philosophical discussion, has been discussed ex- sion making, if not more. In contrast, we consider ethics not tensively in the context of self-driving vehicles (Bonnefon, from the standpoint of an individual agent but of the wider Shariff, and Rahwan 2016). sociotechnical systems (STS) in which the agent operates. Concurrently, there has been an expanding body of work Our contribution in this paper is the conception of ethical STS in the broad AI tradition that investigates designing and governance founded on that takes into account stakeholder verifying, not individual agents, but sociotechnical systems values, normative constraints on agents, and outcomes (states of the STS) that obtain due to actions taken by agents.
  • Proceedings of the 11Th International Symposium on Open Collaboration

    Proceedings of the 11Th International Symposium on Open Collaboration

    Proceedings of the 11th International Symposium on Open Collaboration August 19-21, 2015 San Francisco, California, U.S.A. General chair: Dirk Riehle, Friedrich-Alexander University Erlangen-Nürnberg Research track chairs: Kevin Crowston, Syracuse University Carlos Jensen, Oregon State University Carl Lagoze, University of Michigan Ann Majchrzak, University of Southern California Arvind Malhotra, University of North Carolina at Chapel Hill Claudia Müller-Birn, Freie Universität Berlin Gregorio Robles, Universidad Rey Juan Carlos Aaron Shaw, Northwestern University Sponsors: Wikimedia Foundation Google Inc. University of California Berkeley In-cooperation: ACM SIGSOFT ACM SIGWEB Fiscal sponsor: The John Ernest Foundation The Association for Computing Machinery 2 Penn Plaza, Suite 701 New York New York 10121-0701 ACM COPYRIGHT NOTICE. Copyright © 2014 by the Association for Computing Machin- ery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be hon- ored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or [email protected]. For other copying of articles that carry a code at the bottom of the first or last page, copying is permitted provided that the per-copy fee indicated in the code is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, +1-978-750-8400, +1-978-750- 4470 (fax).
  • Understanding the Challenges of Collaborative Evidence-Based

    Understanding the Challenges of Collaborative Evidence-Based

    Do you have a source for that? Understanding the Challenges of Collaborative Evidence-based Journalism Sheila O’Riordan Gaye Kiely Bill Emerson Joseph Feller Business Information Business Information Business Information Business Information Systems, University Systems, University Systems, University Systems, University College Cork, Ireland College Cork, Ireland College Cork, Ireland College Cork, Ireland [email protected] [email protected] [email protected] [email protected] ABSTRACT consumption has evolved [25]. There has been a move WikiTribune is a pilot news service, where evidence-based towards digital news with increasing user involvement as articles are co-created by professional journalists and a well as the use of social media platforms for accessing and community of volunteers using an open and collaborative discussing current affairs [14,39,43]. As such, the boundaries digital platform. The WikiTribune project is set within an are shifting between professional and amateur contributions. evolving and dynamic media landscape, operating under Traditional news organizations are adding interactive principles of openness and transparency. It combines a features as participatory journalism practices rise (see [8,42]) commercial for-profit business model with an open and the technologies that allow citizens to interact en masse collaborative mode of production with contributions from provide new avenues for engaging in democratic both paid professionals and unpaid volunteers. This deliberation [19]; the “process of reaching reasoned descriptive case study captures the first 12-months of agreement among free and equal citizens” [6:322]. WikiTribune’s operations to understand the challenges and opportunities within this hybrid model of production. We use With these changes, a number of challenges have arisen.
  • Using Shape Expressions (Shex) to Share RDF Data Models and to Guide Curation with Rigorous Validation B Katherine Thornton1( ), Harold Solbrig2, Gregory S

    Using Shape Expressions (Shex) to Share RDF Data Models and to Guide Curation with Rigorous Validation B Katherine Thornton1( ), Harold Solbrig2, Gregory S

    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Repositorio Institucional de la Universidad de Oviedo Using Shape Expressions (ShEx) to Share RDF Data Models and to Guide Curation with Rigorous Validation B Katherine Thornton1( ), Harold Solbrig2, Gregory S. Stupp3, Jose Emilio Labra Gayo4, Daniel Mietchen5, Eric Prud’hommeaux6, and Andra Waagmeester7 1 Yale University, New Haven, CT, USA [email protected] 2 Johns Hopkins University, Baltimore, MD, USA [email protected] 3 The Scripps Research Institute, San Diego, CA, USA [email protected] 4 University of Oviedo, Oviedo, Spain [email protected] 5 Data Science Institute, University of Virginia, Charlottesville, VA, USA [email protected] 6 World Wide Web Consortium (W3C), MIT, Cambridge, MA, USA [email protected] 7 Micelio, Antwerpen, Belgium [email protected] Abstract. We discuss Shape Expressions (ShEx), a concise, formal, modeling and validation language for RDF structures. For instance, a Shape Expression could prescribe that subjects in a given RDF graph that fall into the shape “Paper” are expected to have a section called “Abstract”, and any ShEx implementation can confirm whether that is indeed the case for all such subjects within a given graph or subgraph. There are currently five actively maintained ShEx implementations. We discuss how we use the JavaScript, Scala and Python implementa- tions in RDF data validation workflows in distinct, applied contexts. We present examples of how ShEx can be used to model and validate data from two different sources, the domain-specific Fast Healthcare Interop- erability Resources (FHIR) and the domain-generic Wikidata knowledge base, which is the linked database built and maintained by the Wikimedia Foundation as a sister project to Wikipedia.
  • Classifying Wikipedia Article Quality with Revision History Networks

    Classifying Wikipedia Article Quality with Revision History Networks

    Classifying Wikipedia Article Quality With Revision History Networks Narun Raman∗ Nathaniel Sauerberg∗ Carleton College Carleton College [email protected] [email protected] Jonah Fisher Sneha Narayan Carleton College Carleton College [email protected] [email protected] ABSTRACT long been interested in maintaining and investigating the quality We present a novel model for classifying the quality of Wikipedia of its content [4][6][12]. articles based on structural properties of a network representation Editors and WikiProjects typically rely on assessments of article of the article’s revision history. We create revision history networks quality to focus volunteer attention on improving lower quality (an adaptation of Keegan et. al’s article trajectory networks [7]), articles. This has led to multiple efforts to create classifiers that can where nodes correspond to individual editors of an article, and edges predict the quality of a given article [3][4][18]. These classifiers can join the authors of consecutive revisions. Using descriptive statistics assist in providing assessments of article quality at scale, and help generated from these networks, along with general properties like further our understanding of the features that distinguish high and the number of edits and article size, we predict which of six quality low quality Wikipedia articles. classes (Start, Stub, C-Class, B-Class, Good, Featured) articles belong While many Wikipedia article quality classifiers have focused to, attaining a classification accuracy of 49.35% on a stratified sample on assessing quality based on the content of the latest version of of articles. These results suggest that structures of collaboration an article [1, 4, 18], prior work has suggested that high quality arti- underlying the creation of articles, and not just the content of the cles are associated with more intense collaboration among editors article, should be considered for accurate quality classification.
  • Energy Research & Social Science

    Energy Research & Social Science

    Energy Research & Social Science 70 (2020) 101617 Contents lists available at ScienceDirect Energy Research & Social Science journal homepage: www.elsevier.com/locate/erss Review Sociotechnical agendas: Reviewing future directions for energy and climate T research ⁎ Benjamin K. Sovacoola, , David J. Hessb, Sulfikar Amirc, Frank W. Geelsd, Richard Hirshe, Leandro Rodriguez Medinaf, Clark Millerg, Carla Alvial Palavicinoh, Roopali Phadkei, Marianne Ryghaugj, Johan Schoth, Antti Silvastj, Jennie Stephensk, Andy Stirlingl, Bruno Turnheimm, Erik van der Vleutenn, Harro van Lenteo, Steven Yearleyp a University of Sussex, United Kingdom and Aarhus University, Denmark b Vanderbilt University, United States c Nanyang Technological University, Singapore d The University of Manchester, United Kingdom e Virginia Polytechnic Institute and State University, United States f Universidad de las Americas Puebla, Mexico g Arizona State University, United States h Universiteit Utrecht, Netherlands i Macalester College, United States j Norwegian University of Science and Technology, Norway k Northeastern University, United States l University of Sussex, United Kingdom m Laboratoire Interdisciplinaire Sciences Innovations Sociétés, France n Eindhoven University of Technology, Netherlands o Universiteit Maastricht, Netherlands p The University of Edinburgh, United Kingdom ARTICLE INFO ABSTRACT Keywords: The field of science and technology studies (STS) has introduced and developed a “sociotechnical” perspective Science and technology studies that has been taken up by many disciplines and areas of inquiry. The aims and objectives of this study are Sociotechnical systems threefold: to interrogate which sociotechnical concepts or tools from STS are useful at better understanding Science technology and society energy-related social science, to reflect on prominent themes and topics within those approaches, and to identify Sociology of scientific knowledge current research gaps and directions for the future.
  • E-Posters in Academic/Scientific Conferεnces – Guidelines, Comparative Study & New Suggestions

    E-Posters in Academic/Scientific Conferεnces – Guidelines, Comparative Study & New Suggestions

    University of the Aegean Department of Product and Systems Design Engineering DISSERTATION: E-POSTERS IN ACADEMIC/SCIENTIFIC CONFERΕNCES – GUIDELINES, COMPARATIVE STUDY & NEW SUGGESTIONS Pissaridi Maria Anastasia 511/2004041 Supervisor: Paraskevas Papanikos Committee Members: Nikolaos Zacharopoulos Maria Simosi Syros, June 2015 DISSERTATION: E-POSTERS IN ACADEMIC/SCIENTIFIC CONFERENCES – GUIDELINES, COMPARATIVE STUDY & NEW SUGGESTIONS Supervisor: Paraskevas Papanikos Committee Members: Nikolaos Zacharopoulos Maria Simosi Pissaridi Maria Anastasia Syros, June 2015 3 ABSTRACT Conferences play a key role in getting people interested in a field together to network and exchange knowledge. The poster presentation is a commonly used format for communicating information within the academic scientific conference sector. Paper posters were the beginning but as technology and the way people work changes, posters have to be developed and implemented in order to achieve successful knowledge transfer. Incorporating aspects of information technology into poster presentations can promote an interactive learning environment for users and counter the current passive nature of poster design as an integrated approach with supplemental material is required to achieve changes in user knowledge, attitude and behaviour. After conducting literature review, research of existing e-poster providers, interviews with 5 of them and a personal evaluation in a real time conference environment results show a gradual turn towards e-posters with the medical sector pioneering. Authors, viewers and organisers embrace this new format, and the features and functions it offers, although objections exist since people have different preferences and the e-poster sector is relatively young, an average of 5 years. Pissaridi Maria Anastasia dpsd04041 4 SUMMARY Η ανάγκη των ανθρώπων να συγκεντρώνονται για να ανταλλάσσουν ιδέες, ευρήματα, έρευνες και απόψεις υπήρχε από τη δημιουργία των πρώτων πόλεων όπου φτιάχνονται με χώρους συγκέντρωσης.
  • Science & Technology Studies

    Science & Technology Studies

    ALEXANDRA HOFMÄNNER SCIENCE & TECHNOLOGY STUDIES ELSEWHERE A Postcolonial Programme SCIENCE & TECHNOLOGY STUDIES In April 2017, scientists took to the streets in a historically unprecedented Global March for Science. The event was seen as symbolic of a crisis in the relationship of science and society. This book considers the Global March ELSEWHERE for Science from a postcolonial perspective to inquire into the toolkit that the academic field of Science & Technology Studies (STS) has to offer. It HOFMÄNNER ALEXANDRA argues that new concepts and analytical approaches are necessary to in- A POSTCOLONIAL vestigate current global dynamics in science, technology and society, so as to deliver insights that the recent expansion of STS scholars beyond PROGRAMME Western Europe and North America alone is unlikely to provide. The book presents a Programme in Science Studies Elsewhere (SSE) to demonstrate the urgent need to carry postcolonial issues right into the centre of STS’s intellectual programme. Hofmänner possesses a potent antidote for the field’s inability to see science and technology outside of European or North American experiences. Rayvon Fouché, Professor and Director, American Studies, Purdue University, USA A compelling case for revisiting some of the traditional assumptions in the field of STS. Prof. Dr. Sabine Maasen, Director of the Munich Center for Technology in Society Alexandra Hofmänner is assistant professor in Science & Technology ELSEWHERE STUDIES TECHNOLOGY & SCIENCE Studies ( ST S) at the University of Basel, Switzerland. She received a PhD at the Swiss Federal Institute of Technology ( ETH Zürich ) and has carried out extensive research in Switzerland and South Africa. www.schwabeverlag.de Alexandra Hofmänner Science & Technology Studies Elsewhere A Postcolonial Programme Schwabe Verlag Published with the support of the Swiss National Science Foundation and the Freiwillige Akademische Gesellschaft.
  • An Application of BS ISO 27500:2016

    An Application of BS ISO 27500:2016

    USER EXPERIENCE OF DIGITAL TECHNOLOGIES IN COM CITIZEN SCIENCE A sociotechnical system approach to virtual citizen J science: an application of BS ISO 27500:2016 Robert J. Houghton, James Sprinks, Jessica Wardlaw, Steven Bamford and Stuart Marsh Abstract We discuss the potential application to virtual citizen science of a recent standard (BS ISO 27500:2016 “The human-centred organisation”) which encourages the adoption of a sociotechnical systems perspective across a wide range of businesses, organizations and ventures. Key tenets of the standard concern taking a total systems approach, capitalizing on individual differences as a strength, making usability and accessibility strategic objectives, valuing personnel and paying attention to ethical and values-led elements of the project in terms of being open and trustworthy, social responsibility and health and wellbeing. Drawing upon our experience of projects in our laboratory and the wider literature, we outline the principles identified in the standard and offer citizen science themed interpretations and examples of possible responses. Keywords Citizen science; Participation and science governance DOI https://doi.org/10.22323/2.18010201 Submitted: 4th April 2018 Accepted: 20th November 2018 Published: 17th January 2019 Introduction There is an increasing interest in citizen science as an object of study in its own right and in investigations concerned with how to improve the implementation of citizen science projects in the future [Jordan et al., 2015]. Amongst the key issues are maximizing the quality of volunteer performance [Sprinks et al., 2017], motivating participants to sustain their contributions and to facilitate meeting other project aims also dependent on engagement, typically in terms of scientific outreach and education [e.g., Constant and Roberts, 2017; Dickerson-Lange et al., 2016].
  • Edit Filters on English Wikipedia

    Edit Filters on English Wikipedia

    You Shall Not Publish: Edit Filters on English Wikipedia Lyudmila Vaseva Claudia Müller-Birn Human-Centered Computing | Freie Universität Berlin Human-Centered Computing | Freie Universität Berlin [email protected] [email protected] Figure 1: Warning Message of an edit filter to inform the editor that their edit is potentially non-constructive. ABSTRACT ACM Reference Format: Ensuring the quality of the content provided in online settings Lyudmila Vaseva and Claudia Müller-Birn. 2020. You Shall Not Publish: is an important challenge today, for example, for social media or Edit Filters on English Wikipedia. In 16th International Symposium on Open Collaboration (OpenSym 2020), August 25–27, 2020, Virtual conference, Spain. news. The Wikipedia community has ensured the high-quality ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3412569.3412580 standards for an online encyclopaedia from the beginning and has built a sophisticated set of automated, semi-automated, and manual 1 INTRODUCTION quality assurance mechanisms over the last fifteen years. The scien- tific community has systematically studied these mechanisms but The public heatedly debated so-called "upload filters" in the context 1 one mechanism has been overlooked — edit filters. Edit filters are of the EU copyright law reform in 2019 . Upload filters are con- syntactic rules that assess incoming edits, file uploads or account ceived as a form of copyright protection. They should check for creations. As opposed to many other quality assurance mechanisms, possible infringements of copyrighted material even before a con- edit filters are effective before a new revision is stored in the online tribution is published online — and the contributions affected can encyclopaedia.
  • R. Stuart Geiger Curriculum Vitae September 2021 Stuart@Stuartgeiger.Com | | @Staeiou | Google Scholar | Github

    R. Stuart Geiger Curriculum Vitae September 2021 [email protected] | | @Staeiou | Google Scholar | Github

    Geiger CV 1/14 R. Stuart Geiger Curriculum Vitae September 2021 [email protected] | http://stuartgeiger.com | @staeiou | Google Scholar | GitHub Education University of California at Berkeley, School of Information Ph.D., Information Management & Systems, designated emphasis in New Media December 2015 Georgetown University M.A., Communication, Culture, and Technology May 2009 University of Texas at Austin B.A., Humanities Honors May 2007 Employment University of California at San Diego Assistant Professor, Dept of Communication & Halıcıoğlu Data Science Institute Jul 2020 – present University of California at Berkeley, Berkeley Institute for Data Science Postdoctoral scholar and staff ethnographer Jan 2016 – June 2020 Wikimedia Foundation Research intern (full-time) May 2011 – Aug 2011 Georgetown University Research associate, Communication, Culture, and Technology (full-time) May 2009 – Aug 2010 Publications & Other Scholarly Outputs: Peer-Reviewed Journal Publications: 1. Geiger, R. S., Cope, D., Ip, J., Lotosh, M., Shah, A., Weng, J., & Tang, R. (2021). “Garbage In, Garbage Out” Revisited: What Do Machine Learning Application Papers Report About Human- Labeled Training Data?. Quantitative Science Studies, Online First. 1-32. https://doi.org/10.1162/qss_a_00144 2. Geiger, R. S., Howard, D., & Irani, L. (2021). The Labor of Maintaining and Scaling Free and Open-Source Software Projects. Proceedings of the ACM on Human-Computer Interaction, 5 (CSCW1), 1-28. https://dl.acm.org/doi/pdf/10.1145/3449249 Geiger CV 2/14 3. Scroggins, M.J., Pasquetto, I.V., Geiger, R.S., Boscoe, B.M., Darch, P.T., Cabasse-Mazel, C., Thompson, C. and Borgman, C.L. (2020). “Thorny Problems in Data (-Intensive) Science.” Communications of the ACM.
  • Explaining Sociotechnical Transitions a Critical Realist Perspective

    Explaining Sociotechnical Transitions a Critical Realist Perspective

    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Sussex Research Online Research Policy 47 (2018) 1267–1282 Contents lists available at ScienceDirect Research Policy journal homepage: www.elsevier.com/locate/respol Explaining sociotechnical transitions: A critical realist perspective T Steve Sorrell Centre on Innovation and Energy Demand, Science Policy Research Unit (SPRU), University of Sussex, Falmer, Brighton, BN1 9SL, UK ARTICLE INFO ABSTRACT Keywords: This paper identifies and evaluates the explicit and implicit philosophical assumptions underlying the so-called Multilevel perspective multilevel perspective on sociotechnical transitions (MLP). These include assumptions about the nature of reality Critical realism (ontology), the status of claims about that reality (epistemology) and the appropriate choice of research methods Emergence The paper assesses the consistency of these assumptions with the philosophical tradition of critical realism and Process theory uses this tradition to highlight a number of potential weaknesses of the MLP. These include: the problematic conception of social structure and the misleading priority given to intangible rules; the tendency to use theory as a heuristic device rather than causal explanation; the ambition to develop an extremely versatile framework rather than testing competing explanations; the relative neglect of the necessity or contingency of particular causal mechanisms; and the reliance upon single, historical case studies with insufficient use of comparative methods. However, the paper also concludes that the flexibility of the MLP allows room for reconciliation, and provides some suggestions on how that could be achieved – including proposing an alternative, critical realist interpretation of sociotechnical systems. 1. Introduction foundational assumptions (e.g.