Le droit de la gouvernance pour réguler la gouvernance algorithmique
Bibliographic record
Abstract
Au moment où les algorithmes tendent de plus en plus à nous gouverner, la régulation de la gouvernance algorithmique tarde à prendre forme. Réguler la gouvernance algorithmique pose des défis importants, notamment pour le droit de la modernité juridique. Dans ce contexte, c’est bien souvent l’éthique qui offre les premières pistes d’encadrement. Le droit de la gouvernance se présente comme une alternative à ces deux modes de régulation. Par ses capacités de flexibilité et d’adaptabilité, le droit de la gouvernance semble plus adéquat que le droit moderne pour réguler la gouvernance algorithmique, d’autant plus qu’il constitue le programme juridique du projet cybernétique. Des processus du droit de la gouvernance, tels que la certification, la standardisation et la densification normative, peuvent être convoqués afin de mieux concevoir la régulation de la gouvernance algorithmique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".