Bibliographic record
Abstract
Les romans-mémoires, genre particulièrement en vogue en France dans le premier xviiie siècle, ont la particularité de faire parler un narrateur témoin de sa propre histoire. La valeur du témoignage proposé au lecteur repose alors sur l’articulation entre l’implication du mémorialiste et la prise de distance temporelle et réflexive rendue possible par les caractéristiques narratives du genre. Dans l’Histoire d’une Grecque moderne (1740) cependant, le mémorialiste, qui raconte une de ses « erreurs » – une passion hors norme –, insiste volontiers sur la précarité et les biais qui altèrent son témoignage. L’Histoire d’une Grecque moderne participe ainsi d’un changement d’horizon heuristique du témoignage : à mesure qu’il perd son rôle dans l’élaboration de la vérité, le témoignage se constitue en autre objet de connaissance, susceptible de ne plus délivrer un savoir que sur le témoin lui-même. Nous nous proposons dans cet article d’étudier les conséquences éthico-narratives de ce nouveau régime de l’erreur.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".