Theme Call for Abstracts Digitizing Valuation

Theme Call for Abstracts

Title: Digitizing Valuation

Editors: Francis Lee (Chalmers University of Technology); Andrea Mennicken (London School of Economics); Jacob Reilley (Helmut Schmidt University Hamburg); Malte Ziewitz (Cornell University).

People and objects are increasingly valued and classified using digital technologies (Kornberger et al., 2017). People are valued by digital tools when they job hunt (Reuters 2018), take part in court proceedings (Kirkpatrick, 2016), date (Roscoe & Chillas, 2014), visit their physician (Amelang & Bauer, 2019), gamble (Schüll, 2012), or travel (Neyland, 2018; Jeacle & Carter, 2011). A plethora of objects are also valued digitally: disease outbreaks (Lee et al. 2019), stocks (Muniesa, 2011), email (Kockelman, 2013), climate (Edwards, 2010), web pages (Ziewitz, 2019), crime (Benbouzid, 2019), movies (Hallinan & Striphas, 2016), or entire populations (Cakici, 2016).

The metaphors used to account for these developments have been changing over time. While some have emphasized “the digital,” others have used terms like “algorithmic,” “computational,” or “data-driven.” Most recently, the debate has shifted towards “automation” and “AI.” In all these cases, accounts of worth are generated, distributed, and processed not just by humans but by digital devices – devices that change and challenge how we view the world, how we count and classify, and how we evaluate and establish worth.

Taken together, these changes pose a number of important problems. What happens when we “digitize” practices of valuation and what are the implications? What are the opportunities and limits of existing analytic tools for understanding new forms of digital valuation? What new vistas and perspectives do emerge for research into valuation?

This call invites papers that set out novel, cross-cutting approaches to problematizing, analyzing, and examining the intersection of digitization and valuation in contemporary societies. Contributions are welcome from all empirical areas, including science, markets, government, culture, politics, health, and everyday life. Potential research themes include, but are not limited to:

  • Linkages between digitization, valuation, and accountability
  • The relationship between digital and other modes of valuation 
  • Issues of bias, discrimination, and gaming in digital valuation
  • Innovative methodologies for studying digital valuation
  • The roles and relevance of automation in digital valuation
  • The relationship between digital valuation and new modes of governing
  • How different modes of sensing the world (digital or otherwise) are valued
  • How agency is redistributed by digital valuation systems
  • Questions of privacy, ownership, and control
  • Due process, recourse, and contestability
  • Historical analyses of digital valuation practices 
  • ...

Expressions of interest shall be submitted in the form of an extended abstract to the editors (about 1,000 words). Selected authors will then be invited to submit full papers for peer review. 

Important dates

September 15, 2020:               Extended abstracts due 

October 15, 2020:                    Notification of authors

April 15, 2021:                           Full papers due

Submit your abstract here:

Questions can be directed to 

Valuation Studies sets out to offer an academic platform for research and debate on the problem of valuation. Valuation indeed stands as a crucial problem for the social sciences and the humanities today, in more than one way. Understanding the tensions, determinants, contexts and effects of valuation practices appears indeed as a decisive requirement for the understanding of how our world is constructed, transformed or fractured. An interdisciplinary approach is required in order to investigate the technical cultures, the political imaginaries, the historical processes, the methodological problems and the institutional settings that shape the ways in which things are valued, and to identify relevant shifts, controversies and struggles. Sociological, anthropological, cultural, political, semiotic, historiographic, legal, institutional, critical, organisational approaches to the study of valuation phenomena are needed in order to establish tractable, actionable interdisciplinary knowledge on valuation as a problem. Submissions to Valuation Studies respond to broad calls for contributions curated by the journal’s editorial board. This is to ensure focus and debate, while offering the space to address each call’s purpose from many possible angles and in reference to various forms of evidence and demonstration. The journal assesses incoming manuscripts using double-blind peer review involving at least two reviewers. Accepted manuscripts are published as PDFs with full open access and authors retain the copyright to their work.

Bibliography of Selected References

Amelang, K., & Bauer, S. (2019). Following the algorithm: How epidemiological risk-scores do accountability. Social Studies of Science, 49(4), 476–502. 

Benbouzid, B. (2019). Values and consequences in predictive machine evaluation. A sociology of predictive policing. Science & Technology Studies, 32(4), 119–136.

Cakici, B. (2016). Peopling Europe: How Data Make a People (ARITHMUS) Method Workshop Report: Tracing Algorithmic Practices. 1–11.

Duclos, V. (2019). Algorithmic futures: The life and death of Google Flu Trends. Medicine Anthropology Theory, 6(3), 54-76.

Dudhwala, F., & Björklund Larsen, L. (2019). Recalibration in counting and accounting practices: Dealing with algorithmic output in public and private. Big Data & Society, 6(2), 1-12.

Edwards, P. N. (2010). A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. MIT Press.

Fourcade, M., & Healy, K. (2017). Categories all the way down. Historical Social Research, 42(1), 286–96.

Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems 14 (3), 330–347.

Hallinan, B., & Striphas, T. (2016). Recommended for you: The Netflix Prize and the production of algorithmic culture. New Media & Society, 18(1), 117–137.

Jeacle, I., & Carter, C. (2011). In TripAdvisor we trust: Rankings, calculative regimes and abstract systems. Accounting, Organizations and Society, 36(4), 293-309.

Kirkpatrick, K. (2016). Battling algorithmic bias: How do we ensure algorithms treat us fairly? Communications of the ACM, 59(10), 16–17.

Kockelman, P. (2013). The anthropology of an equation. Sieves, spam filters, agentive algorithms, and ontologies of transformation. HAU: Journal of Ethnographic Theory, 3(3), 33–61.

Kornberger, M., Pflueger, D., & Mouritsen, J. (2017). Evaluative infrastructures: Accounting for platform organization. Accounting, Organizations and Society, 60, 79-95.

Lee, F.,Bier, J., Christensen, J., Engelmann, L., Helgesson, C.-F., & Williams, R. (2019). Algorithms as folding: Reframing the analytical focus. Big Data & Society, 6(2), 1–12.

Lee, F., & Björklund Larsen, L. (2019). How should we theorize algorithms? Five ideal types in analyzing algorithmic normativities. Big Data & Society, 6(2), 1-6. 

Lee, F., & Helgesson, C.-F. (2019). Styles of valuation: Algorithms and agency in high-throughput bioscience. Science, Technology, & Human Values, in press, 1-27.

Mennicken, A., & Espeland, W. N. (2019). What’s new with numbers? Sociological approaches to the study of quantification. Annual Review of Sociology, 45, 223-245.

Muniesa, F. (2003). Des marchés comme algorithmes: sociologie de la cotation électronique à la Bourse de Paris. École Nationale Supérieure des Mines, Paris.

Muniesa, F. (2011). Is a stock exchange a computer solution?: Explicitness, algorithms and the Arizona Stock Exchange. International Journal of Actor-Network Theory and Technological Innovation, 3(1), 1–15. 

Neyland, D. (2018). The Everyday Life of an Algorithm (1st ed. 2019 edition). Palgrave Pivot.

Pasquale, F. (2016). The Black Box Society: The Secret Algorithms That Control Money and Information. Reprint edition. Cambridge, Massachusetts, London, England: Harvard University Press.

Reuters. 2018. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women,” October 10, 2018.

Roscoe, P., & Chillas, S. (2014). The state of affairs: Critical performativity and the online dating industry. Organization, 21(6), 797–820.

Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2016). When the algorithm itself is a racist. International Journal of Communication, 10, 4972–4990.

Schüll, N. D. (2012). Addiction by design: Machine gambling in Las Vegas. Princeton: Princeton University Press.

Ziewitz, M. (2019). Rethinking gaming: the ethical work of optimization in web search engines. Social Studies of Science 49(5): 707–731.