Mémoire historique et autofiction : Un roman d’Allemagne (2016) de Régine Robin
Bibliographic record
Abstract
Cette contribution analyse l’ouvrageUn roman d’Allemagne(2016) de Régine Robin dans une triple perspective :d’abord à travers la mémoire historique de l’histoire allemande du XXesiècle qu’il reconstruit en ayant recours à de multiples traces dans les médias, allant de correspondances et de cartes postales conservées, jusqu’à des noms de rues et des témoignages oraux; puis en questionnant la vision de l’Allemagne qui est construite dans cet ouvrage, mettant en avant l’histoire des victimes oubliées, des résistants et des persécutés, notamment du régime nazi; et, enfin, en analysant les rapports complexes entre fiction et non-fiction entre historiographie et littérature, qui caractérisent la structure narrative et discursive de cet ouvrage hybride et expérimental de Régine Robin.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".