Littérature et opportunisme sous l’Occupation. L’exemple de l’écrivain et éditeur français Jean de La Hire (1878-1956)
Bibliographic record
Abstract
Jean de La Hire, auteur français célèbre pendant la première moitié du xxe siècle pour ses romans populaires, est devenu éditeur en prenant, de manière surprenante, la tête des Éditions Ferenczi, qu’il a aidé à aryaniser. Nous examinons ici les raisons qui l’ont poussé à renier son engagement auprès des républicains socialistes pour devenir le chantre du nazisme. Nous remarquerons que cet engagement brutal aux côtés de la Collaboration a plus été le fait d’un froid opportunisme que d’une conviction sincère — ce qui eut des conséquences sur sa gestion de la maison d’édition. S’il publie bien certains ouvrages de propagande, et s’il en rédige lui-même certains, il n’en reste pas moins que sa gestion de Ferenczi reste minimale, et qu’il ne touche en fait qu’à la marge de son catalogue. La Hire incarne ainsi un visage différent de l’engagement, moins sincère, bien moins humaniste, et bien plus confortable.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".