The Political Theory of Data: Institutions, Algorithms, & Formats in Racial Redlining
Bibliographic record
Abstract
Despite widespread recognition of an emergent politics of data in our midst, we strikingly lack a political theory of data.We readily acknowledge the presence of data across our political lives, but we often do not know how to conceptualize the politics of all those data points-the forms of power they constitute and the kinds of political subjects they implicate.Recent work in numerous academic disciplines is evidence of the first steps toward a political theory of data.This article maps some limits of this emergent literature with an eye to enriching its theoretical range.The literature on data politics, both within political theory and elsewhere, has thus far focused almost exclusively on the algorithm.This article locates a further dimension of data politics in the work of formatting technology or, more simply, formats.Formats are simultaneously conceptual and technical in the ways they define what can even count as data, and by extension who can count as data and how they can count.A focus on formats is of theoretical value because it provides a bridge between work on the conceptual contours of categories and the technology-centric literature on algorithms that tends to ignore the more conceptual dimensions of data technology.The political insight enabled by format theory is shown in the context of an extended interrogation of the politics of racialized redlining.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.126 | 0.459 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".