Surveillance et suspicion à l’ère numérique. Réflexions à partir de la politique mondiale contre l’argent sale
Bibliographic record
Abstract
La montée en puissance de l’intelligence artificielle et des algorithmes comme nouvelles figures du pouvoir de surveiller et d’agir à partir de vastes masses de données est devenu un enjeu incontournable en matière de sécurité et de contrôle social. Au cours des vingt dernières années, soit la période d’existence de Champ Pénal, ce sujet a en effet pris une importance considérable. Pourtant, il brille paradoxalement par son absence dans les publications de la revue. Alors que le numéro anniversaire constitue une occasion idéale pour commencer à y remédier, le présent article vise à poser un premier jalon en ce sens, et ce à la lumière des systèmes algorithmiques de surveillance et de suspicion déployés dans le cadre de la politique mondiale contre l’argent sale.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".