The Promise of Precision: Datafication in Medicine, Agriculture and
Education
This paper analyses how precision has become a ubiquitous prefix in medicine,
agriculture and education. The accompanying imagination in each of these domains is
that ‘data’ will enable greater predictive accuracy through new sensors and interfaces.
In this paper, we aim to provide insights regarding the ways in which precision
assemblages function to augment and extend existing knowledge and data
infrastructures, whilst also being underpinned by the anticipatory promise of the
ubiquity of digital and sensing technologies. We argue that precision is marked by new
data production and aggregation frameworks to measure and intervene. At the same
time, precision draws on – and augments – established clinical, agricultural and
educational subjectivities in ways that depict new logics of patient, student and
environmental care. As we outline below, the threshold of the shift to precision is
articulated and institutionalized at different points in each field we analyze in the
subsequent sections of this paper – namely medicine, agriculture and education. This
suggests that precision should be understood as an unevenly realized moment in policy
development, rather than as simply produced through processes of technological
change.
Keywords: precision medicine, precision agriculture, precision education, data
infrastructure, datafication, value
Introduction
‘Precision’ has recently emerged as an increasingly common descriptor of specific domains of scientific and technological research and deployments in advanced industrial nations.
1 From the Obama-era Precision Medicine Initiative, to John Deere and Bayer-Monsanto’s promises of smart farming through precise fertilizer use, to ‘AttenivU’ tracking devices in classrooms, promises of optimised outcomes abound. The central claim across these domains is that new ‘precision’ technologies will add new forms of knowledge to, and further shape, data-led policy-making (Williamson 2019b). Contemporary promoters of precision medicine, agriculture and education promise policy-makers, owners of capital, students and other subjects unprecedented control over the manifold risks and opportunities that confront their institutions—hospitals, farms, classrooms. This paper sets out a preliminary attempt to compare the articulation of precision across diverse policy settings in medicine, education and agriculture. We argue that the promise and imagined value of precision is situated as both a critique of existing knowledge infrastructures and as an assertion of the emergent possibility of novel responses for new, ‘wicked’ or complex problems. Our objective in this paper is to compare three domains of vital importance to the management of populations, each with different historical relations to concepts of precision and attendant infrastructure.
Precision is, somewhat ironically, a marker of displacement into a vaguely articulated future rather a signifier of present location. Proponents of precision in multiple policy domains predicate claim its value lies in both its future value as new associations are made visible through various automated analytics. This displacement into the future is key to the speculative political economy of data whereby assumed future use and exchange value is presented as the justification for gathering data in the present. At the same time, data is presented as highly mobile, and its sharing is a precondition for and feature of contemporary business models and policy rationales. In this sense, the emergent and fluid landscape of health, education and agricultural data that we discuss below constitutes a horizon of both political possibility and contestation (Lehtiniemi and Haapoja 2019). Articulations of precision in healthcare, education and agriculture are premised on the imagined value of data
2 infrastructures in producing novel outcomes in the context of existing policy settings. The curation of new data infrastructures in these domains is heralded as providing the conditions for new forms of knowledge, practice and evidence, and valorisation. Across numerous domains, the production of new data in ‘precision’ fields are accompanied by, or in some cases depend upon, the use of sensors which are claimed to increase certainty of knowledge and decision-making.
All three domains – medicine, agriculture, and education – have been transformed by datafication: the translation of things such as brain activity, genes and soil chemicals into numbers (Mayer-Schönberger and Cukier 2013). Such translation is achieved by what Miles
(2019), writing in the context of the development of precision agriculture systems, describes
as an “epistemological relationship between industrialization and information” characterized
by “algorithmic rationality, which … expresses the normative grammar of modern capitalist production” (p. 10). In a similar vein, Sharon (2018) argues that the ‘Googlization of health
research’ constitutes a “new model of multi-stakeholder, data-driven health research that is
emerging at the intersection of digital health and digital capitalism, and which must be
situated in the broader context of the political economy of data sharing” (p. 10). Our concept
of datafication emphasises the ways the generation of digital data, to be exploited through
commercial analytics in the ways Miles and Sharon explicate, has itself become a rationale
for policy-making.
This rationale is epistemological, infrastructural and promissory: the development and
introduction of digital infrastructures across each domain is tied to an emergent political
economy of data that both supports and is shaped by infrastructures of algorithmic data use.
By this we mean that precision is not simply premised on the integration of digital
3 technologies into material practices of healthcare, education and agriculture. But rather that
the promise of precision is based on the anticipation that new forms of knowledge – and
commercial opportunity – will emerge from the compilation of diverse forms of data. We suggest that these processes in turn reinforce the role of contemporary commercialization models in ways that structure the research and development on and implementation of precision technologies. We conclude the paper with observations about what emerges from these three domains, namely that: in all three there are attempts to extract new value from existing infrastructures; that precision produces evidence of its own efficacy; and, that precision is part of the invention of accuracy.
Hacking (2016) characterizes the technologization and bureaucratization of numerical statistics in the nineteenth century as the ‘avalanche of printed numbers’. He contends this
“statistical enthusiasm” was animated by the impulses of a conservative philanthropy that aimed at “the preservation of the established state” (p. 281). In the sections that follow we extend this line of analysis by suggesting that the promissory horizon of precision anticipates both the extrapolation of contemporary modes of bureaucratic governance and the novel renewal of relations between the state and its subjects. The movements we document in this article, such as data cooperatives and farm hack collectives, underscore the political nature of the assumptions about what is measured and how it is measured in precision agriculture, education and medicine. As we will argue below these counter currents are suggestive of the ways in which the ethical and normative contestation of big data and precision systems – often conceptualized in terms of ownership, privacy and control – is central to the realization of value in precision systems (Bronson 2020).
4 Precision Medicine and the Political Economy of Health Data
A recent New York Times article by Natasha Singer entitled ‘How Big Tech Is Going After
Your Health Care’ begins with the story of a young medical student, Daniel Poston, who upon opening the app store on his iPhone, sees an app for a new heart study prominently featured. Singer’s article explains the significance of this development for the contemporary medical landscape. She writes: “People often learn about new research studies through in- person conversations with their doctors. But not only did this study, run by Stanford
University, use a smartphone to recruit consumers, it was financed by Apple. And it involved using an app on the Apple Watch to try to identify irregular heart rhythms.” Intrigued, the medical student, who already owned an Apple Watch, registered for the heart study. Poston muses about whether “the entire practice of medicine will be revolutionized by technology” by the time he finishes his medical degree. Indeed, Singer’s article describes how Apple,
Google, Microsoft, Intel, Amazon and Facebook and other large tech companies have started making significant investments – some $2.7bn – in healthcare equity deals. Many of these investments aim to disentangle medical interventions from clinics through the development of continuous monitoring devices such as the Apple Watch or devices that detect movement in the owner’s bed (Apple has also acquired Beddit, a sleep tracking company).
These examples represent a new form of the ‘datafication of health’. The novelty of this turn in the governance of health refers to both a sensibility and an accompanying set of practices related to digital devices, especially through smart phones and cloud computing. Proponents claim these developments will revolutionise therapy development, diagnosis and care—a sensibility that is explicitly outlined in the US National Academies of Sciences Toward
Precision Medicine report (National Research Council 2011), which articulates the need for a
5 new data commons to aggregate genomic, social and environmental data1 with a view to
developing diagnostics and therapeutics for precise targeting down to the individual level.
The involvement of public institutions through information brokerage augments the narrative
of individual empowerment through consumption in a marketplace of devices (Prainsack
2017).
New taxonomies of disease are premised on an “’Information Commons’ in which data on
various biomedical and lifestyle characteristics of large populations of patients become
broadly available for research use. Such a commons is intended to subsume these multiple
data forms to allow researchers and clinicians to diagnose new associations between
biological, lifestyle and chemical associations and abnormal health outcomes. Such work
then constitutes a ‘Knowledge Network’ that adds value to these data by highlighting their
inter-connectedness and by integrating them with evolving knowledge of fundamental
biological processes.” (National Research Council 2011, 2). This ‘Information Commons’
would help to overcome the problem that “the current discovery model offers no path toward
economically sustainable integration of data-intensive biology with medicine” (National
Research Council 2011, 61). Some four years after this landmark report, then President
Obama announced the ‘Precision Medicine Initiative’ which promised to build on
individualised approaches to medicine such as blood typing for transfusions and scale them
up using “large-scale biologic databases (such as the human genome sequence), powerful
methods for characterizing patients (such as proteomics, metabolomics, genomics, diverse
cellular assays, and even mobile health technology)” (Collins and Varmus 2015).
1 The report prefigures the All of US program insofar as it revolves around a large voluntary biobank designed to reflect the diversity of the US population.
6 A common denominator of major precision biomedicine proposals in both emerging and
post-industrial economies – especially the USA, China, and the UK – is that they marry environmental and social data infrastructure with imagining sensors down to the nano-scale
in an attempt to understand diseases as complex and regulated by a multitude of factors. The
promissory horizon of precision is one where the patient is finally ‘put back together’
following centuries of health paradigms premised on treating symptoms, lesions or defective
biomarkers (Greene and Loscalzo 2017). This promise renders a new form of holism,
manifest in an ontology rendered explicit in the marketing of precision medicine data analysis
platforms (Vogt, Hofmann, and Getz 2016). For example, Microsoft’s launch of a platform in
early 2019 promised to “take in data from several sources so that it can be analyzed in a
holistic way and, eventually, allow the hospital to tailor care down to the individual.”2
Similar promises are made in Google, Apple and Amazon marketing material.
The new holistic vision of precision medicine databases is dependent on a “field of atomized
parts, including genes, proteins, digital data sources, and computational infrastructures”
which renders some things visible and others invisible (Greene and Loscalzo 2017, 2497).
This new whole is both made stable and contested through new power hierarchies in clinical settings between patients, clinicians and technology providers (Topol 2016, Prainsack 2018).
The imagined user of precision medicine technology in these settings is both an empowered consumer of diverse age, cultural background and health status; and a clinician for whom new sensing devices and algorithm rules support clinical judgements (Topol 2016).
However, the imagined subject of precision medicine oscillates between a figure of an individual patient, and a broader population-wide designation. For example, the somewhat
2 See https://www.geekwire.com/2019/microsoft-ucla-reveal-cloud-platform-use-healthcare-data-precision- medicine/ (accessed 1 Sept 2019)
7 clunky terminology of precision was jettisoned in 2018 with the relaunching of the
programme as the ‘All of US’ initiative: an “historic effort to gather data from one million or
more people living in the United States to accelerate research and improve health”, that
promises to “uncover paths toward delivering precision medicine” by “taking into account
individual differences in lifestyle, environment, and biology”3. The joining of public and
private institutions through the ‘All of US’ initiative seeks to generate what Beckert has
termed ‘promissory legitimacy’—an anchoring of “political authority [in the] credibility of promises with regard to future outcomes that political (or economic) leaders make when justifying decisions” (Beckert 2019, 1). At the same time the moniker “All of Us” seemingly carries overlapping designations of political subjectivity, nationhood, and imagined collective identify in ways that signal the “kaleidoscopic ways in which definitions of individual and collective rights both influence and are transformed by changes in the biological status of the human” (Jasanoff 2011, 11)
Legitimating Precision Biomedicine: Extracting or Generating Biovalue?
Precision medicine is marked by an intensification of promises to both develop individualised diagnosis and therapies, and a unified whole (‘All of US’) through the imaginary of datafication and linkage. Rather than positing that ‘precision’ will capitalise on the Human
Genome Project with personalised medicine, its proponents offer a vision of mass customization through data aggregation (Prainsack 2017, Tutton 2012). Proponents of precision biomedicine in government health agencies and research institutes promote an understanding of health and illness beyond genomics by seeking to integrate environmental, socio-economic and population group parameters into databases powered by a participatory
3 See: https://allofus.nih.gov/ (accessed 23 December 2019).
8 ethos of citizen engagement, especially through activity tracking devices and deliberative forums (Morgan, Nordhaus, and Gottlieb 2013, Blasimme and Vayena 2016, Ginsburg and
Phillips 2018). While this integration is premised on the promise of new therapies and cures, pharmacogenomic treatments have not materialised for all but a few specific mutations and conditions such as cystic fibrosis (Tutton 2014).
Precision is thus not just a terrain of new knowledge of disease, it is also a speculative horizon for therapy companies to develop new devices and medications. Many nation-states are implementing policies that integrate genomic data with other information in ways that promise targeted or individualised treatments, generating both health and wealth. This quest to build ‘biovalue’ (Cooper and Waldby 2014, Cooper 2011) – the surplus-value of life across a population – over recent decades has seen a proliferation of biobanking projects that incorporate multiple forms of data, especially in Europe and Asia (Bühler, Barazzetti, and
Kaufmann 2019, Tutton 2007, Gottweis, Chen, and Starkbaum 2011). As Waldby (2002) argues, the development of “biotechnolog[ies] frequently destabilize and reconstitute naturalized relations between bodies, bodily fragments, human identities and social systems”
(p. 308) in ways that sustain the production of “a margin of biovalue, a surplus of fragmentary vitality” (p. 310). Biovalue is not a stable property of tissue samples such as blood, tumour excisions or hair, but rather “a capacity of tissues to lead to new and unexpected forms of value” (Waldby and Mitchell 2006, 108). The liveliness of the sources of biovalue derives from both their intimacy and their deep biological capaciousness (Cooper and Waldby 2014, Waldby 2002). Genes are at once a foundational biological material and deeply personal – with as few as 30-80 genes required to identify a human (Naveed et al.
2015). As gene-focused biobanking is increasingly seen to have failed on its promises through concerns about the ‘biobanking bubble bursting’ (Chalmers et al. 2016), precision biomedicine breathes new life into the promises of a post-industrial bio-economy (Gardner
9 and Webster 2017) and its attendant assumptions of economic growth. Notions of precision, in other words, are mobilised to stake out firm, fertile ground in the shifting terrains of life
upon which the bioeconomy operates.
Struggles over ownership of intellectual property, data access rights, and other assets are a
defining element of the economies of precision biomedicine and, as we discuss below, are
critical to the articulation of precision in agriculture and education. Indeed, the relationship
between the practices of the life sciences and ownership over biological materials has been an
important theme of social scientific analysis. Much of this work views ownership broadly in
its institutional and historical settings (Radin 2017, Parry and Greenhough 2018). As Jasanoff
(2011) argues, ownership can be understood in a cascade of broadly legal questions raised by
genetic and genomic technologies, such as the potential for harm, the trade-off between individual and group rights, and the separation between human and nonhuman. Questions of ownership are pivotal to the constitution of fields in the life sciences, such as synthetic biology (Calvert 2012). Patents are pivotal ‘intangible assets’ in the creation of biovalue and are thus deeply political in their capacity to spark disputes about identities and group boundaries (Parthasarathy 2017, Birch, Tyfield, and Chiappetta 2018). In this sense, the development of data assets relies on circumscribing access, governance and control to a small number of corporate actors whose valuation of the data is premised on its exchange value, which may be latent or realised through transactions in clinical or corporate settings.
Precision as both a critique of existing research paradigms and a vision for nation-building
rightfully provokes counterproposals. Thus, the question of whether and how ownership over
resources can align with democratic objectives is an important, if less extensively discussed
topic in this field. Many entrepreneurs, researchers and legal practitioners see health data,
therapy development and healthcare labour not as passive resources for capitalist
10 exploitation, but a battleground in a vision for a more democratic economy. Landmark aged care and other nursing worker cooperatives are becoming flagships for the ‘platform cooperative’ movement (Schneider 2018), whilst data creation and ownership are key frontiers in the evolution of new business models broadly attached to the vision of a ‘sharing economy.’4 These sites place individualised monitoring as a key site of contestation between capitalist and sharing economy alternative business models. As we document below in the case of farm hack collectives, activist modes of engagement with datafication, including resistance, are integral to precision discourse.
Precision Agriculture
Visions of data-integrated ‘precision’ farming incorporating sensor systems, geospatial data and real-time yield monitoring technologies predate the development of precision biomedicine in the popular press by some twenty-five years. These new assemblages of technologies have been articulated to address overlapping concerns for food security, agricultural productivity, climatic change and soil erosion. With the advent of ubiquitous mobile computing, precision farming now exists at the nexus of mobile apps, decision support tools aimed at individual farmers, and the proliferation and concentration of both farm-level and geospatial data enabled sensor technologies embedded in machinery such as tractors (Bronson 2018, 2020). Low cost sensors and interfaces ostensibly realise a discourse found in one of the earliest mentions of precision agriculture whereby the Wall Street Journal flagged a new assemblage of controlling and monitoring agricultural inputs and outputs:
4 See Morgan and Kuch (2015) for an analysis of ‘collaborative consumption’ discourse that mutated into what is commonly considered the sharing economy.
11 Since the early 1900s, U.S. farmers have relied on ever bigger machines and ever
more chemicals to enlarge their crops—and profits. But the new trend is toward
‘precision agriculture’. Researchers see the day when machines armed with
microcomputers and sophisticated mapping devices move through fields, adjusting
water, chemical and fertilizer applications according to the nature of the soil. (Wall
1986, np)
From these incarnations in the 1980s and 1990s, research and development in precision agriculture has served to consolidate the civilian redeployment of a series of information systems technologies that had initially been developed in military contexts (MacDonald
2007). As Wolf and Buttel (1996) suggest, the “commercialization of precision farming
technologies has had as much to do with the search for and promotion of new uses of existing
non-agribusiness manufacturing and/or military technology as it has had to do with the new
technology being induced by economic or social ‘signals’” (p. 1269). In recent work Belcher
(2019) traces the development of geographic information systems and remote sensing technologies during the Vietnam War, through a series of technologies “predicated on a location-based event ontology, significant insofar as social geographical phenomena could be disclosed at a local level” where a “key variable in these new digital systems was the use of a standard geographical reference point – Universal Transverse Mercator (UTM) coordinates – assigned to reported activity and inputted into military computers” (p. 2).5 From the mid-
1990s, visions of precision agriculture heralded a form of technologically-enabled farming
that would improve farm yield and efficiency whilst limiting soil degradation and pollution.
5 See also Kaplan (2006) on the implicit militarism of precision technologies, whereby “geographically based location technologies that draw on discourses of precision make possible the subjects of both consumption and war” (p. 696).
12
In addition to providing a means through which military technologies might be civilised, the
development of precision agriculture might also be understood as “both products and
reinforcing elements of the political economy of contemporary agriculture” (Wolf and Wood
1997, 184). In this sense, the development of precision agriculture and ‘smart farming’
technologies are situated in interlocking social and political dynamics that shape
contemporary agriculture. Critical here are ways in which precision agriculture systems
reinforce the concentration of ownership and control over agricultural production, whereby
“farm businesses continue to get larger and fewer in number” (Lockie 2015, 12). This leads
to a bipolar distribution on farming operations “with a small number of large-scale commercial businesses producing the majority of agricultural output while small-scale farms
– which make up an overwhelming majority of agricultural businesses – account for a small proportion of output” (p. 18).
Debt relations between farmers and large corporate suppliers are often mediated through technological dependencies that have deepened over time. The increasing financialization of
agricultural sectors across the world has seen the indebtedness of many farming businesses increase. As such, the development and commercialisation of precision agriculture technologies by subsidiaries of major agribusinesses – particularly John Deere and Bayer-
Monsanto – serves to reinforce the concentration and dependence on these conglomerates
(Bronson 2020). As Higgins et al. (2017) succinctly state, precision agriculture systems are a form of “social ordering in which farmers, to remain viable, must become increasingly dependent on the commercial inputs supplied by these firms” (p.197).
13 For this reason, critical appraisals of precision agriculture have concluded that these
developments are “consistent with a productivist science, and it represents an attempt to
rationalize and legitimate chemically dependent, thermodynamically inefficient, and
ecologically destructive patterns of production in an era of increasing environmentalism”
(Wolf and Wood 1997, 186). More generally political and environmental critiques of
precision agriculture speak of big data as the new “digital DNA” of precision agriculture
(ETC Group 2018), and have warned of the concentration of precision agriculture and farm data “in the hands of big agribusinesses [in ways that] limits the potential of this technology,
and only reinforces the aims of a few corporations and their investments” (Carbonell 2016,
8). The seemingly enthusiastic promotion of precision agriculture has also been tempered by
a recognition of the practical challenges posed both by implementing these systems in
biophysically heterogeneous environments and the possibility for adverse social and
environmental consequences. Fraser (2018) warns that these developments “are creating new
data points (about flows, soils, pests, climate) that agricultural technology providers ‘grab’,
aggregate, compute and/or sell” (p. 1). The proposition that big data technologies are “created
within a complex social and political assemblage that actively shapes its constitution”
(Kitchin 2014, 5) has led to calls for the development of governance frameworks and for the
responsible development of precision agriculture systems (Bronson 2018).
Precision Agriculture as environmental sensing
Precision agriculture represents the confluence between two major streams of agricultural
R&D: i) the automation of farming through the deployment of GPS, navigational and
guidance technologies; and, ii) the consolidation of farm-level big-data systems for real-time
environmental sensing, yield monitoring and the development of variable rate technologies
for the application of farm inputs (typically chemical fertilizer and synthetic pesticides). For
Miles (2019) this confluence:
14
Might be grouped into two general fields, the biological and the mechanical. The first
and earlier was the computerization of agricultural biology: the digital apprehension
of genetic matter and the development of information-based tools for biological
management, experimentation, and manipulation of seeds, livestock, pesticides and
hormones. The second encompasses farm machinery and the insinuation of
computerized information technology into farm management, from satellites and
tractors to drones and machine learning algorithms, and is popularly known as
precision agriculture (PA). Together, these developments mean that most aspects of
conventional farming, from seeds to harvesters to the “supply chain” itself, are
increasingly understood through and executed with information, data, and digital
media technologies (p. 2).
Public investments in technologies across both these broad fields have been critical to
realising private gains in profits. Public investments in long-term environmental monitoring,
geospatial mapping and remote sensing constitute the foundation infrastructures for
contemporary precision agriculture. Similarly, policy programmes designed to improve
access to broadband internet services in rural areas – often underpinned by public subsidies
intended to address the rural ‘digital divide’ (Hennessy, Läpple, and Moran 2016, Townsend
et al. 2013) – are a critical means through which public investment is coordinated to enable
the commercial development of precision agriculture systems. Data is assembled only
through concerted strategies to make certain land connected to servers. For example, a recent
report issued by the UN Food and Agriculture Organisation (FAO 2019) warns of that the
“digitalization of the agrifood system involves the risk that the potential benefits will be unequally distributed between rural and urban areas, gender, youth population” and further
15 that “urban areas often have better developed ‘digital ecosystems’ compared with rural areas”
(p. 2). In response FAO recommends a series of public policies that provide the “basic
conditions for [a] digital transformation” of farming practice and agri-food production. These include public advocacy for “infrastructure and connectivity (mobile subscriptions, network coverage, internet access, and electricity supply), affordability, educational attainment
(literacy, ICT education) and institutional support” (p. 3).
As we have documented in the domains of medicine (above) and education (below) the development and implementation of precision systems is enabled by the projection of student, patient and clinical subjectivities. Similarly, the integration of precision agricultural systems into the material infrastructure of contemporary farming has been enacted through, and coded by, subtle shifts in farmer subjectivities. Though often projected through the imagined figure of the ‘good farmer’, this culturally specific and situated account of farmer subjectivity is being transformed from a figure who is “supposed to know their land” (Tsouvalis, Seymour, and Watkins 2000, 922) to a figure who knows their land digitally. While the figure of the
‘good farmer’ is commonly deployed as a trope in efforts to incentivise the adoption of smart farming technologies this figure appears as increasingly data-savvy (Higgins et al. 2017,
Carolan 2018, Gardezi and Bronson 2019). In this way the development and dissemination of precision agriculture is premised on an appropriation – and technological augmentation – of the figure of the farmer as an embodied environmental sensor. The good farmer who “pays attention to the weather” (Morton, McGuire, and Cast 2017, 18), and cares “for the soil, water, plants, and animals under the [their] supervision” (Thompson 2005, 73), is imagined as an algorithmically assisted subject.
16 The promotion of precision agriculture systems strategically presents digital technologies as
decision support tools. This secondary status to human decision-making parallel the ways the
representation of precision medicine technologies oscillate between representations of
technologies as supporting and augmenting existing healthcare practices and an alternative
depiction that casts precision medicine as a new form of healthcare in its own right. The
possibilities for forms of digitally enabled and collaborative environmental sensing and land
management remain latent in precision agriculture. One reason for this latency is a focus on
individual private farmers, imagined to extract maximal returns, whilst also promising a kind of technological supplementation through the automation of a range of farm practices
(Carolan 2018). In this model, the farmer is transformed from being a manager of farm inputs and outputs to becoming an operative for, and an interpreter of, farm data exchanges between land and distant servers.
At the same time the projection of technologically-assisted famer subjectivity is entangled with the re-negotiation of the cultural trope of the good farmer. For example, in her classic study of projections of masculinity in Norwegian tractor advertisements Brandth (1995)
explores subtle shifts in the gendered registers of agricultural labor encoded in depictions of
farm machinery. Brandth tracks changes in these projections associated with the introduction
of computer enabled farm equipment. She comments that in place of tropes associated with
“’heavy’ work relevant to such masculine notions as strength and hard physical labour … the
new, masculine images of farming are constructed to match a more scientific form of agriculture” (p. 130).6 This projection of a data-savvy farmer identity is also imagined as
6 Recent work has demonstrated the ways in which “male farmers use agricultural technology to reinforce patriarchal ideologies, which marginalize and exclude women from farming” (Saugeres 2002, 143) whilst the emergence of data-savvy farmer subjectivity has provided a means for the proliferation of more diverse subjectivities and the possible empowerment of rural women (Hay and Pearce 2014).
17 producing new relations with non-human farm subjects. Care for animal bodies retains an embodied and subjective intimacy whilst being increasingly augmented by digital and sensor technologies (Bear and Holloway 2015).
The implied transformations of agricultural labour enabled by precision farming have led to recent calls for research on the possible exploitation of marginalized agricultural labourers
(Rotz et al. 2019), and has been accompanied by initiatives designed to regulate the use of farm data, and the coordination of codes of conduct intended to mediate relationships between agricultural producers and agriculture technology providers.7 For example, the
American Farm Bureau Federation (AFBF) recently negotiated a series of Privacy and
Security Principles for Farm Data with a number of large agri-business interests. The agreement, which is designed to encourage “ongoing engagement and dialogue regarding this rapidly developing technology” includes principles that encompass issues such as
‘Ownership’, ‘Collection, Access and Control’, and ‘Data Retention and Availability’
(American Farm Bureau Federation 2015). A similar initiative developed in New Zealand – the Farm Data Code of Practice – provides a series of principles for the access to and use of farm data, including codes of practice designed to regulate ‘Rights to Data’, ‘Data
Interchange & Access’, ‘Data Security’ and ‘Regulatory Compliance’ (Farm Data
Accreditation Limited 2016). Recent reviews of the development of precision agriculture have found that “the legal and regulatory frameworks around agricultural data are piecemeal, fragmented and ad hoc” (Wiseman and Sanderson 2018, 1) and further that “informed and proactive dialogue” (Fleming et al. 2018, 24) between researchers, industry, government and
7 Broader work in this space has explored the possible development of norms for ‘responsible agricultural innovation’ in precision agriculture (Bronson 2019, 2018, Rose and Chilvers 2018).
18 producers will be necessary in mediating the development and adoption of precision
agriculture technologies.
Meanwhile, developments in precision agriculture have also been taken up by activist and
grassroots innovation communities. For example, Farm Hack is “a worldwide community of
farmers that build and modify our own tools”8 and who share these tools through online
networks of agricultural producers and land managers. Part of a broader movement in ‘eco-
digital commoning’ (Bollier 2012), that Morgan and Kuch (2016) depict as characterised by a
“focus on mutuality and reciprocity” where notions of “interdependence take priority over
extractive dominance” (p. 1717), Farm Hack was established through a collaboration between
M.I.T. and the US National Young Farmers Coalition. Combining the ethos of open-source
software development with neo-agrarianism, Farm Hack collectives are present in Australia,
Europe and the USA, and aim to develop participatory platforms engaged in knowledge exchange and agricultural stewardship. Farm Hack collectives provide platforms that aim to avert socio-technical lock-in, thereby embodying a direct challenge to concentrated ownership of agricultural and environmental data.
Precision education and neuro education technologies
If precision medicine and agriculture are attempts to generate new forms of private value in
the shadow of US Federal investments in research, Precision Education’s relation to legacies
around the military industrial complex tend to be second-hand interpretations of the foci of
medicine around personalization. As Williamson posits, ‘[e]mergent ideas and practices of
‘precision education’ build on techniques of
personalized learning, such as learning analytics and adaptive learning software, but
8 Cox (2015) & Farm Hack (no date).
19 also encompass ideals associated with “precision medicine” and “personalized healthcare”’
(p. 6).
Precision in education has an historical antecedent in ‘precision teaching’, a classroom-based
practice emerging in the 1980s that was connected to behavioural psychology and aimed at regularly measuring performance in learning thorough analogue techniques, and by providing data-based interventions in the classroom (West, Young & Spooner, 1990). While the focus on data and measurement continues in contemporary precision education it is now a digital undertaking that has expanded to include genetics, neuroscience, and cognitive psychology.
‘Precision education’ is framed as a ‘new interdisciplinary educational science focused on the quantification of students’ affects, bodies and brains’ (Williamson, 2019, p.1), that is characterized by ‘sociotechnical ensembles of scientific expertise, data-intensive technologies, research labs, business interests, philanthropic support and policy advocacy that constitute the new data-intensive learning sciences’ (Williamson, 2019, p. 3).
Proponents claim to generate new knowledge for diagnosis, intervention and learning improvement, requiring attendant policy-making to support the new technologies and metrics. The promise of precision education is to “foresee the concerted use of learner data
for purposes of implementing individualized educational practices and ‘targeted learning’”
(Williamson 2019c, no pagination). This knowledge base creates new forms of scientific data to be used in the existing policy environment of education that focuses on data-driven decision making, coupled with individual choice—primarily of school provision but increasingly of personalised learning (Lupton and Williamson 2017, Williamson 2019a,
Authors). Precision education merges machine learning and adaptive computing with new data capabilities and infrastructure that are part of a push, often driven by education
20 technology corporations, for differentiated educational provision. Much of what seems to be
passing as precision education is connected to the burgeoning education technology sector
and venture capital site, the latter passing $500 million in 2015.
While precision education has covered areas of behavioural genetics, in this section we focus
on the area of education neuroscience that focuses on the neural processes related to learning
using “techniques and technologies of neuroscience (e.g., functional magnetic resonance
imaging (fMRI), electroencephalography (EEG)) in studies of learning processes” (Pykett
and Disney 2015, 8). Educational neuroscience promises increased and new knowledge about
“how the brain ‘works’ and hence how learning happens, and about why different learners
learn differently” (Pykett and Disney 2015, 1). New data produced through neuroscience are
being introduced into the existing data-focused realm of education, and ‘new neurotechnologies appear to open up the ‘learning brain’ not just for inspection and inscription, however, but to new forms of
prescriptive policy intervention and even direct modification’ (Williamson, 2019, p. 9). As
such, in the 21st century, new data infrastructures have been built with technology companies
now part of digital education governance.
Accompanying educational neuroscience has, therefore, been the rise of educational
neurotechnology A key part of the start-up and venture capital market includes products that use sensors that tap into data infrastructures and can be seen to extract value from incremental and often imperceptible changes. Different products claim to provide not only
new knowledge but accessible and actionable knowledge for application in schooling
settings. Some sensors in education technology are commonplace, such as cameras that can
be used to track student movements in classrooms. However, others are part of products that
21 promise technological innovation informed by neuroscience research. For neuroscience this has meant a burgeoning research field can grow alongside an emergent neurotechnology market in education.
The most prominent, commercially- plausible, sensor technology is what is termed ‘brain- computer interfaces’ (Williamson 2019a, 68), some of which are replicating, shrinking and repurposing existing technology. For example, MIT researchers have developed a prototype
‘AttenivU’ which is modelled on a pair of sunglasses that can be worn if the user wants to increase attentiveness. This product combines two kinds of sensors: measuring brain activity via electroencephalography (EEG) and eye movement tracking via electrooculography
(EOG), that claim to measure when attentiveness is low and use audio or haptic stimulus to nudge users to be attentive. The researchers claim that results of a prototype have produced improved learning outcomes (Morris 2019).
Mass education, precision intervention and affirmation
If precision education aims to extract new value from human potential, we suggest that it is still based within systems of mass education which are characterised by an orientation towards human capital production and economic outcomes. The latter is understood as both national economic contributions, global competitiveness and individual competitive advantage and social mobility (Cranston et al. 2010).
Precision education is not only extracting data for educational purposes, but also shifting value from educational institutions to new capital markets. In neuroscience it is equating learning with visualisation, and Pykett (2016) proposes that the pre-eminence of international comparisons for education and the creation of human capital as the dominant purpose of education, “make neuroeducation … an attractive prospect for educators operating within a
22 discursive framework in which there is a repeated promise to improve, accelerate, enhance
and increase the quantity of learning” (p. 113). This is leading to new areas of expertise and
evidence-making that are locating education policymaking in the realm of the expanding data science field—and in which the ‘digital saturation’ of life is increasingly challenging the qualitative and proximate premise of evidence in education (see de Freitas 2017). Precision is
making its way into education policy making via international organisations such as the
OECD, that ‘are increasingly turning to data from the brain sciences as knowledge of how
young people learn as a way of recommending policy interventions’ (Williamson, 2019, p.
11).
While still in its infancy, and indeed quite ill-defined as a discrete field, two key critiques and
sites of contestation are emerging about the possibility of precision education and sensor
technologies in education. One is quite straightforward—that of intervention, surveillance
and limited claims, such as reducing learning to algorithms (see for example, Williamson and
Piattoeva 2019). This critique is a response to the production of new data directly related to
the individual bodies and brains of students (and teachers) that becomes connected via
infrastructures to the possibility of intervention. The second critique is an affirmative one in
which there are investments and desires in the use of sensors (de Freitas 2017). That is, in
highly individualized realms like education where the combination of choice and education
as a positional good means that precision education is not just about the governance of
populations via mass schooling, but the governing of the self, in that precision education, is
part of the quantified self, the working of the self as calculation.
Conclusion: Emergent precisions
In this paper, we have identified three domains in which precision has emerged as a new
descriptor of scientific and technological research and implementation. The comparison
23 between these three domains is valuable in at least three ways. Firstly, it allows us to
understand parallel dynamics through common imaginaries of data infrastructures; that is,
through the common use of similar hardware and software or cloud computing technologies,
such as those provided by Microsoft. Secondly, comparison demonstrates the uneven nature
of precision’s deployment: the ways in which the infrastructures, promises and logics of precision emerges in an irregular fashion amongst entrepreneurs in commerce and policy-
making who seek to accomplish social and political outcomes through greater targeting. A
common denominator, especially in relation to agriculture and medicine, relates US Federal
Government and Military spending on large research projects, to which ‘precision’ seeks to
develop new forms of value extraction. Thirdly, comparative analyses makes apparent that
the ethical and normative problematization of precision – centred on questions of ownership,
oversight and control – is a central element of the construction of value across each domain.
Here, the construction of policies to support new data gathering requires the resolution of
such questions, often in uneven ways. Of the three domains we focus on precision education
is the least developed area, but as we noted it borrows heavily in imagery of sensors and
value construction from precision medicine and as such it is likely to follow similar
trajectories.
It is for this reason that we suggest that the critical interpretation of precision technologies
needs to be informed by a sensibility that attends to ways in which precision emerges as an
object of promissory social and political work. Viewed historically, a concern with the
relationship between modes of bureaucratic organization and the affordances of precision
instrumentation and measurements emerged in the 18th century and was further consolidated
in the 19th and early 20th Centuries (MacKenzie 1990). As historian M Norton Wise (1995a) suggests, the historical “values of precision” were put forward as “responsible, non-
24 emotional, objective and scientific” (p. 1). Yet the paradox was that the accomplishment of precision was “a remarkable cultural achievement” (1995b, 359) setting conventions that become “simultaneously agents of unity and products of agreement” (p. 360). Seen in this light, notions of quantification and enumeration are coded with the explicitly cultural and political values of accountability and transparency (Porter 1995, Desrosières 1998); “if you can count something you can also account for it” (Jasanoff 2017, 1, emphasis in original). As
Olesko (1995) documents the ways precision cemented “a decidedly masculine variety of honesty, integrity, and work” (p. 126) in nineteenth century industrial Germany.
In recent years the scope of promising related to how precision might solve social problems has expanded both temporally and spatially. It is for this reason that the value of precise measurement has become less certain, and remains an emergent promissory horizon. The instantiation of precision infrastructures – commonly under the rubric of big data – seemingly lack the kinds of collective “agreement about what is valued and how it is to be valued”
(Wise 1995a, 7) that fueled earlier efforts of mass data collection and collation. Similarly the foundational epistemic presumption that data assemblages give rise to seamlessly portable forms of knowledge is increasingly contested. As we argue below, the assumed legitimacy of precision assemblages is of a promissory form (Beckert 2019). This means that the value of precision is cast as an end in itself, whilst remaining emergent in the consolidation of precision infrastructures. The irony at the heart of the development of precision technologies in each of the domains we address below is that the assemblage of ‘big data’ systems has become a performative value in its own terms; a value that will, we are assured by proponents and developers, be realized in the future tense. It is this ambiguity that we argue makes the promise of precision a key site of political possibility and contestation.
25 Policy Implications of the Turn to Precision
There are opportunities for policy learning across each of these domains, as we outline below. In medicine, agriculture and education, precision provides both a critique of existing infrastructures and an assertion of the promise of technological fixes for problems within these infrastructures. While these are preliminary and tentative thoughts, they also indicate the basis, and perhaps need, for future empirical investigations. The first area that requires further investigation is that in each domain there is an extracting of new value within pre- existing infrastructural values. Precision appears as an intensification of the dynamics that have shaped the political-economic relations that subtend each of the sectors we have focused on in the paper. At the same time a sociotechnical imaginary of precision – coded with notions of accuracy, prediction and quantification – functions to secure capital, knowledge and populations. We have identified that there are large centralised infrastructures that are reaching their limits in turn of value extracted. Precision creates a re-imagination of how value can be extracted in incremental ways, leveraging and mutually constituting contemporary policy and political conditions to introduce technological innovations via a new configuration of sensing and corporate participation. Precision thus promises to both identify and extract previously unknowable surplus value. It promises this through mass customization via data aggregation, which forms a set of narrowly framed innovations that will benefit individual students, patients and farmers.
Medicine Agriculture Education
Conditioning Limits of Marketization of Augmentation of antecedents pharmacogenomics Military behaviourist
Technologies education approaches
26 Key promises Diagnosis through Better land Linking brain
data linkage, clinical management, activity to learning
augmentation efficient chemical outcomes through
use sensors
Critical responses Data Cooperatives Farm Hack Emerging
and data commons Collectives surveillance concerns
movements
Imagined Subjects Patient AND Farmer (usually Teacher (usually
clinician male) female) and students
Key sensing devices Wearable devices, Satellite remote Wearable devices,
diagnostic algorithms sensing and RFID headband EEGs
Table 1: Comparison of Precision Medicine, Agriculture and Education
Second, to fulfil this promise of innovation, precision needs to produce evidence about its
own efficacy. This evidence is always produced relatively to preceding regimes. For
example, as we have argued above, the developments of precision are cast as a new step-wise change with regards to two components. Firstly, the creation, concentration and maintenance of sensing networks and big data infrastructures is cast as both a normative good in itself and a matter of national political importance. In this context the evidentiary tactics deployed to demonstrate both the practical and policy efficacy of big data infrastructures are shaped and conditioned by competing imaginations of how power should be distributed and who are legitimate participants in the adjudication of precision.
This promise to extract value and the evidence of efficacy is supported by our final aspect, which pertains to the invention of accuracy. The addition of ‘precision’ and the use of sensors often mask unacknowledged policy commitments, such as the valuing of highly industrialised
27 and mechanised agricultural production processes premised on externalising environmental costs such as fertiliser runoff. It is for this reason that policy advocates are likely to oversell the value of precision whilst at the same time participating in the creation of social, economic and political structures that enabled the fulfilment of this promise.
Policymaking requires the adjudication of evidence in multiple settings: regulatory decision- making; standards-setting; and establishing funding priorities for research and development, infrastructure and skills and training. In each of these settings, we have emphasised the ways a turn to precision is embedded in communities of practice, ethics and aesthetics that have been contested in fundamental ways. Table 1 summarizes the diverse origins, justifications and counterproposals in each of these domains. The contestation of the efficacy of ‘precision’ technologies is never simply contained within a mutually accepted frame. For example, there is a fundamental tension between ‘precision agriculture’ and the regenerative agriculture posit competing visions of holistic management through differing ideas of inclusion. Our plea to policymakers is to understand not only the trade-offs between legitimacy and efficiency implied by these tensions (such as how much time and effort to put into consent processes for data gathering) but to foster visions of public good that keep the significant power of large oligopolies in check through anti-trust and appropriately responsive regulation. Policy making should attend to the political implications of precision by understanding it as a vision to restructure resources in ways that may delegitimise other socially desirable ways of life.
28 References
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33