Bibliographic record
Abstract
Inspiré d’un fait divers de 1839, le roman Kamouraska d’Anne Hébert raconte un meurtre, non pas comme événement objectif et extérieur, mais comme événement vécu et remémoré, sur un mode résolument traumatique, par l’héroïne Élisabeth d’Aulnières, complice du crime. En analysant cette représentation hébertienne de la mémoire, et qui plus est la manière dont le texte, à l’aide de procédés formels, exhibe ou mime le fonctionnement même des processus mémoriels, cet article entend réinscrire le roman dans la généalogie des représentations (orales et littéraires) du fait divers entre 1839 et 1970, généalogie dont Anne Hébert est à la fois l’héritière directe, pour des raisons familiales, et la critique, en tant que romancière : il s’agit de montrer, en un mot, que le roman peut être lu comme une représentation du fonctionnement de la mémoire collective devant les événements traumatiques dont elle conserve la trace.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".