La lutte contre la cybercriminalité à l’échelle de l’Union : analyse de l’évolution juridique d’un phénomène à la confluence de plusieurs agendas institutionnels
Bibliographic record
Abstract
La lutte contre la cybercriminalité à l’échelle de l’Union européenne naît du constat que, face à un phénomène d’ampleur transnationale, ses États membres ne peuvent agir de manière isolée. Une action spécifique se structure dès lors progressivement. S’il était davantage question de « criminalité informatique », le terme de cybercriminalité va peu à peu s’imposer, au fur et à mesure que l’action de l’Union se singularise. La lutte contre ce phénomène tend à se trouver à la convergence d’agendas institutionnels qui favorisent une dynamique intégrative puissante : le marché numérique européen, l’espace pénal européen, la cybersécurité et la sécurité intérieure, notamment.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".