Management algorithmique et dépendance économique d’une main-d’œuvre racisée
Bibliographic record
Abstract
Alors que les chauffeurs Uber sont des travailleurs indépendants présumés qui bénéficient théoriquement d’une forte autonomie au travail, ils se voient soumis à une nouvelle forme de contrôle exercé par la plateforme. Le management algorithmique combine ainsi des instruments de pouvoir relevant de la logique disciplinaire et de la logique gouvernementale pour orienter leurs comportements. S’appuyant sur une enquête réalisée auprès de chauffeurs à Paris, Londres et Montréal, cet article se propose d’appréhender la manière dont se manifeste concrètement ce management algorithmique, les ressorts de son efficacité, mais également les résistances que les chauffeurs sont susceptibles de lui opposer. Il démontre ainsi que son efficacité ne repose pas tant sur les caractéristiques techniques des dispositifs mis en œuvre que sur la dépendance économique à la plateforme d’une main-d’œuvre racisée.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".