Becoming-Infrastructure: Datafication, Deactivation and the Social Credit System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How might critical library and information studies analyze the intersection of information infrastructure and class structure? The emergence of big data through "datafication" rests on the historical process of information and communication technology (ICT) production and distribution. This paper explores the concept of datafication as an integrated component of information infrastructures unfolding within the class structures of capitalism. A critical realist perspective on relational sociology is offered to illustrate how heterogenous data sources are combined and configured to activate materials and bodies into new internal economic class relations of control. My analysis of datafication therefore moves beyond isolated conceptions of "information" and toward the capacity of distributed data sources to extend and deepen class structures. Two recent large scale cases of datafication are analyzed to highlight its causal powers within class structured society. The first case is drawn from a New York Times article concerning the subprime automobile loan market in the United States. The article details the installation of surveillance technologies into the vehicles of people segmented by low credit scores as a condition of exchange for subprime loans. As a result of this exchange, surveillance technologies capture borrower's driving behaviors and locations in real-time data flows. These data flows are analyzed according to interest bearing payment regimes, rendering both vehicle and borrower as manageable assets while conferring onto lenders the power of remote automobile deactivation. This suggests datafication of driving behaviour produces new implications for class conditions when such data are integrated with the structures of the subprime market. The second case detailed in several news articles examines the plan for a large scale top-down cybernetic behavioural programming initiative by the Chinese government termed the "social credit system," built from digital traces of multiple economic and non-economic social behaviours of its citizens. While aspects of this system are currently voluntary, they are expected to become mandatory within five years. Ubiquitous surveillance of digital activity never before combined into a predictive and prescriptive score may be considered a nation-wide disciplinary subsumption of social activity under novel valuation algorithms, integrating previously unwatched or irrelevant external activities into new internal relations determinative of class structured possibilities. The plan for a social credit system appears driven toward developing a seamlessly interconnected national behavioural identity for every Chinese citizen, which may produce structural implications for pre-existing class conditions. I suggest these cases are examples of the need for library and information studies to engage critically with the emerging causal powers of information infrastructures theorized here as deepening capitalism's control of class structures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.012 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it