Bibliographic record
Abstract
De La belle Bete publie en 1959 jusqu’a la serie Soifs achevee en 2018, l’oeuvre de Marie-Claire Blais compte plus d’une soixantaine de titres, romans, recueils de poemes, pieces de theâtre et essais, qui n’ont cesse d’etonner par le renouvellement des themes, des formes et des voix narratives qui en font la singularite. Regroupant les travaux presentes lors des « Journees internationales Marie-Claire Blais » qui ont eu lieu a Quebec en novembre 2016, cet ouvrage reunit des etudes aux perspectives variees qui se repondent et se relancent. Divise en cinq parties – « Parcourir », « Editer », « Commenter », « Traduire », « Accompagner » –, il soumet l’oeuvre a l’epreuve des lectures actuelles et la revisite pour en souligner l’importance et l’originalite radicale. A l’approche « savante » des universitaires se joint le point de vue des editeurs, attaches a la fabrication du livre et aux defis de sa mise en marche, et celui des traducteurs qui s’interessent aux enjeux politiques de la traduction, a son histoire et a sa pratique. Les reflexions et les temoignages de trois ecrivaines de generations differentes et liees a des esthetiques distinctes s’inscrivent dans le dialogue litteraire que suscite l’oeuvre de Marie-Claire Blais et en illustrent la contemporaneite particuliere, si justement accordee aux epoques successives qu’elle traverse.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.022 |
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".