La gouvernance polycentrique du cybercrime : les réseaux fragmentés de la coopération internationale
Bibliographic record
Abstract
L’une des caractéristiques fondamentales de la délinquance numérique est sa nature transnationale, qui semble constituer un obstacle majeur à l’harmonisation et la coordination de ressources policières, par définition locales. L’étude empirique de la gouvernance internationale du cybercrime nous offre cependant une image bien différente de la situation. Cet article utilise la méthode de l’analyse des réseaux sociaux (ARS) afin de modéliser la structure polycentrique des acteurs et des initiatives qui incarnent la coopération anti-cybercriminalité. En se basant sur un corpus de 657 acteurs organisationnels participant à 51 initiatives, on applique la technique des réseaux d’affiliation (ou réseaux à deux dimensions) pour mesurer la cohésion du réseau global, identifier les acteurs publics et privés occupant un rôle central dans ce dispositif, ainsi que ceux jouant un rôle d’intermédiaire (ou de broker) entre des sous-groupes géographiques ou fonctionnels relativement segmentés.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".