Big Data et quantification de soi : La gouvernementalité algorithmique dans le monde numériquement administré
Bibliographic record
Abstract
This article aims to discuss, from a critical theory perspective, the apparent paradox between the ability to monitor and control made possible by Big Data and the belief in the latter’s emancipatory potential. The goal is to assess to what extent the data, tools and methods characteristic of Big Data constitute self-regulation processes that are similar to a dynamics of governance and surveillance. We will analyze this phenomenon on the basis of two concepts inherited from the critical tradition in social science, namely that of the administered world as formulated by the Frankfurt School theorists, and that of governmentality as developed by Michel Foucault.
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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 | 0.026 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".