Bibliographic record
Abstract
L’erreur judiciaire est, au pénal, une tragédie épouvantable. Elle déshonore tous les personnels qui y concourent : les juges qui ont commis l’irréparable les avocats incapables de la prévenir les enquêteurs désignant un innocent à l’opprobre de la justice. Fondé sur l’étude de dizaines de décisions anglo-saxonnes et sur l’accès direct aux dossiers d’instruction français, cet ouvrage tente de comprendre les mécanismes d’une telle catastrophe par une comparaison entre les erreurs judiciaires des pays d’outre-Manche et d’outre-Atlantique et celles, réelles ou supposées, de la France. Les premières (par exemple, pour la Grande-Bretagne, les Birmingham Six pour les États-Unis, Randall Adams pour le Canada, Donald Marshall Jr, etc.) prouvent que la conception même de la procédure porte une responsabilité terrible dans les désastres judiciaires. L’étude des affaires françaises définitives (Seznec, Deshays, Dils, Omar Raddad, Outreau) et l’évocation de celles qui surviennent maintenant (Marc Machin, Dany Leprince) montrent ce que la vérité a d’insaisissable. Ce livre illustre aussi – et surtout – l’incroyable faiblesse humaine, celle des juges, des avocats, des enquêteurs et – on le découvre – celle des accusés.
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 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".