Bibliographic record
Abstract
Temoin d’actes reprehensibles ou estimes tels, ne s’est-on pas interroge : devrais-je rapporter ces faits a la police ? Denoncer mon voisin, mon collegue, mon patron ? Devrais-je le faire systematiquement par principe, ou plutot au cas par cas, quand la situation est dangereuse ? A partir de quel moment devient-on un traitre, un lâche ? Ou a l’inverse un citoyen actif qui participe au respect de la securite civile ? Le « fayot », le rapporteur, le delateur ont mauvaise presse. On en trouve de bien sinistres exemples dans notre histoire recente, et pas seulement dans les pays de l’Est ! Mais les forces de la loi et de l’ordre ont subi des mutations. La conception d’une democratie transparente, des scandales comme celui d’Enron, la menace terroriste, les nouvelles technologies ont change la donne. La loi Perben 2 sur les indics, l’incitation des salaries a denoncer les fraudes dans l’entreprise, les citoyens relais… on prone une surveillance devenue democratique et citoyenne, les whistle-blower et l’alerte ethique ont le vent en poupe. Specialistes de l’information policiere et sociologues de la police, magistrats, historiens et politiques, debattent ici des enjeux d’une « surveillance citoyenne ».
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.016 |
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; both teacher heads agree on what is shown here.
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".