Figures tutélaires, textes fondateurs
Bibliographic record
Abstract
Cet ouvrage analyse le rôle de figures tutélaires dans l’émergence et la légitimation des littératures francophones : De Coster en Belgique, Ramuz en Suisse romande, Amrouche en Afrique du Nord, Senghor en Afrique subsaharienne, Césaire aux Antilles, Miron au Québec. Il propose dans le même temps une lecture de l’héritage critique à travers les textes fondateurs qui ont marqué un tournant décisif dans l’histoire par les visions politiques et les interrogations qu’ils ont suscitées et par l’imaginaire qu’ils ont nourri ou développé. Comment cet héritage est-il aujourd’hui accueilli et conceptualisé ? Des critiques et des écrivains, notamment Marie-Claire Blais, Jacques Chessex, Hélène Doiron, Sylviane Dupuis, Eugène Ébodé, Majid El Houssi, Nabile Farès, Jean Louvet, Daniel Maximin, Jean Métellus, Pierre Mertens, Tierno Monénembo, Clara Ness, Dominique Noguez, Leïla Sebbar, ont suggéré des lignes de force d’une histoire littéraire inédite, dont les langues sont à la fois des enjeux et des médiums.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".