Big Data, gouvernementalité et industrialisation des médiations symboliques et politico-institutionnelles
Bibliographic record
Abstract
Dans cet article, nous interrogeons l’intégration des médias socionumériques au sein de dynamiques globales de production, de circulation, de captation et de traitement de données (Big Data). Plus spécifiquement, nous analysons comment ces dynamiques contribuent au déploiement d’un nouveau mode d’objectivation social. Il s’agit, d’une part, de montrer comment ce mode d’objectivation repose sur l’intégration des médias socionumériques au sein de circuits marchands. D’autre part, nous éclairons comment le processus d’industrialisation du traitement des données personnelles induit de nouvelles modalités de régulation sociale fondée sur des procédés algorithmiques. Nous présentons enfin comment se traduit cette « gouvernementalité algorithmique » sur le plan des représentations symboliques constitutives de l’« objectivité » que prennent les rapports sociaux et de la signification que les sujets accordent à leurs pratiques.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".