Les Cahiers des Dix : à la recherche d’une vérité historique à transmettre
Bibliographic record
Abstract
Le premier numéro des Cahiers des Dix paraît en octobre 1936. Comme il est reçu avec enthousiasme et qu’il se vend très rapidement, la Société des Dix répète l’événement chaque année, offrant au lectorat intéressé par l’histoire un numéro original et des textes variés. À partir d’une revue de presse qui couvre de la naissance des Cahiers jusqu’à 1960, soit les 25 premiers numéros, cet article propose de jeter un regard sur le travail des membres de la Société, sur la réception des Cahiers annuels et sur les commentaires publiés à chacune de leur parution. Comment se présentaient les Dix et que faisaient-ils valoir de leurs Cahiers ? À leurs voix s’ajoutent celles de fidèles collaborateurs qui les soutiennent, les défendent, les louangent, dont quelques femmes qui prennent la parole avec force.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".