Vers un virage algorithmique de la lutte anticartels ?
Bibliographic record
Abstract
Depuis les travaux séminaux d’Ezrachi et Stucke (2015, 2016) sur la collusion algorithmique, la question des ententes appuyées voire suscitées par des algorithmes de prix occupe une place importante dans la littérature d’économie industrielle et du droit de la concurrence. L’objet de cette contribution est d’analyser dans quelle mesure les algorithmes de surveillance pour assurer le contrôle et la détection d’éventuelles ententes algorithmiques soulèvent eux-mêmes des enjeux non seulement dans les champs économiques et juridiques mais également éthiques. Dans cet article, nous souhaitons analyser à la fois les opportunités et les risques pour les autorités de la concurrence de l’utilisation d’une « preuve algorithmique » que ce soit au niveau de l’explicabilité du résultat et de possibles biais pouvant s’introduire dans le résultat final ainsi qu’au niveau de leur redevabilité pour expliquer leurs décisions.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".