Prospecting (in) the data sciences

Slota, Stephen C.; Hoffman, Andrew S.; Ribes, David; Bowker, Geoffrey C.

doi:10.1177/2053951720906849

Cited by 32 publications

(44 citation statements)

References 32 publications

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“…Anthropologists and qualitative social scientists have long raised concerns about data-driven techniques on epistemological, ethical and political grounds (Iliadis and Russo, 2016;Manovich, 2012), arguing that they fail to capture the nuance of situated practice (boyd and Crawford, 2012) or lead us toward simplistic research questions (Uprichard, 2013), that they exacerbate existing asymmetries of access and visibility (Benjamin, 2019) and that they remain largely unaccountable (Pasquale, 2015) to the people whose lives they affect. STS scholars in Moats particular have examined how algorithms and data analytics achieve their performed neutrality, commensurability of different types of data (Slota et al, 2020) and gloss over gaps and silences (Coopmans, 2014;Leonelli et al, 2017;Lippert and Verran, 2018;Neyland, 2016).…”

Section: The Current Settlementmentioning

confidence: 99%

Rethinking the ‘Great Divide’

Moats

2020

S&TS

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It is often claimed that the rise of so called ‘big data’ and computationally advanced methods may exacerbate tensions between disciplines like data science and anthropology. This paper is an attempt to reflect on these possible tensions and their resolution, empirically. It contributes to a growing body of literature which observes interdisciplinary collabrations around new methods and digital infrastructures in practice but argues that many existing arrangements for interdisciplinary collaboration enforce a separation between disciplines in which identities are not really put at risk. In order to disrupt these standard roles and routines we put on a series of workshops in which mainly self-identified qualitative or non-technical researchers were encouraged to use digital tools (scrapers, automated text analysis and data visualisations). The paper focuses on three empirical examples from the workshops in which tensions, both between disciplines and methods, flared up and how they were ultimately managed or settled. In order to characterise both these tensions and negotiating strategies I draw on Woolgar and Stengers’ use of the humour and irony to describe how disciplines relate to each others truth claims. I conclude that while there is great potential in more open-ended collaborative settings, qualitative social scientists may need to confront some of their own disciplinary baggage in order for better dialogue and more radical mixings between disciplines to occur.

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Section: The Current Settlementmentioning

confidence: 99%

Rethinking the ‘Great Divide’

Moats

2020

S&TS

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“…Meaningful interpretation requires in-depth knowledge of a given domain. Where the naïve data scientist simply prospects a new subject domain (Slota et al 2020), he or she may overlook the impact of key policy and legislative milestones on any time series analysis. As (Slota et al 2020) write, data science can appear 'curiously empty' -where interdisciplinary research is approached as an act of solicitation or simple extraction in a new domain.…”

Section: The Socio-legal Contributionmentioning

confidence: 99%

Scaling up research on family justice using large-scale administrative data: an invitation to the socio-legal community

Broadhurst

Cusworth

Harwin

et al. 2021

Journal of Social Welfare and Family Law

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This article outlines the value of administrative data for family justice research. Although socio-legal scholars have extended their research beyond purely theoretical or doctrinal analyses, studies using large-scale digital datasets remain few in number. As new opportunities arise to link large-scale administrative datasets across health, education, welfare and justice, it is vital that the community of family justice researchers and analysts are supported to deliver research based on entire service or family court populations. In this context, this article provides a definition of administrative data, before outlining the potential of single, linked or blended administrative data sets for family justice research. The remaining sections of the article speak to questions that are pertinent to this particular academic community, including the distinctive contribution of the socio-legal scholar to interdisciplinary teams and the place of data providers in collaborative research. Drawing on the sociological concept of 'publics', the final section considers the multiple interest groups whose social licence must be secured, when personal records are used to understand the relationship between law and family life.

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“…Taking people into account as much as possible then implies a particular organization which consists, as for Pasteur in the canonical article by Bruno , in transporting the world into the closed and controlled enclosure of the (data) laboratory. During what Slota et al (2020) describe as the "prospection" phase, data scientists audit existing data infrastructures to gather and organize usable data for their projects. To this end, they organize meetings where multiple authorized spokespersons from the world of the customers are invited, in order to include their points of view in the design of measurement projects.…”

Section: The Client and Her Spokespeoplementioning

confidence: 99%

Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge

Kotras

2020

Big Data & Society

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This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose to study the original ambivalence that characterizes the promise of a mass personalization, i.e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing. This paper argues for the study of algorithms in their contexts and suggests new perspectives on algorithmic objectivity.

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Prospecting (in) the data sciences

Cited by 32 publications

References 32 publications

Rethinking the ‘Great Divide’

Rethinking the ‘Great Divide’

Scaling up research on family justice using large-scale administrative data: an invitation to the socio-legal community

Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge

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