L’enfermement dans les pratiques de big data : une interprétation par la théorie sociale critique
Bibliographic record
Abstract
Les géants du capitalisme numérique exploitent des pratiques de big data reposant sur la datafication de nos comportements, sur l’accès permanent à ces données et sur leur traitement par apprentissage automatique. Nous nous enfermons dans ces pratiques et les plateformes associées sans en être pleinement conscients. Cet article propose une théorie de la dynamique causale de cet enfermement représentée à la fois par des boucles de renforcement et synthétisée par trois propositions. L’idéologie de la technique (Marcuse, 1968) conduit le développement d’une fausse conscience (Heidegger, 1954) qui conditionne l’enfermement numérique et conduit à des marchandages faustiens. Tant la fausse conscience, que cet enfermement et les marchandages faustiens sont l’objet de boucles causales de renforcement délétères et inter-reliées constituant une explication plausible de la diminution des libertés des utilisateurs du numérique.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.048 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".