Bibliographic record
Abstract
Cet ouvrage rassemble les contributions de Nancy Huston a l'hebdomadaire Le 1, offrant l'opportunite au lecteur d'apprecier la plume de la romanciere et l'esprit acere de l'essayiste sur des sujets aussi varies que la politique internationale, la politique interieure francaise ou encore la langue francaise. Depuis l'aube de l'humanite, nous dit Nancy Huston, notre espece superpose a la realite des recits simplistes et manicheens : les bons et les mechants, les gagnants et les perdants. Face aux attentats de Paris, au rejet des migrants et a la logique guerriere dans laquelle le pays s'engouffre, face a l'election de Trump et au Brexit, elle nous invite a prendre la bonne distance en substituant la reflexion aux schemas binaires qui masquent la realite. Nee en 1953 a Calgary, dans le Canada anglophone, Nancy Huston, romanciere et essayiste, est arrivee en France a l'âge de 20 ans pour poursuivre des etudes de linguistique. Elle ecrit ses premiers romans en francais, puis commence a pratiquer des allers-retours entre sa langue maternelle et sa langue d'adoption, notamment en traduisant elle-meme ses romans. Elle a publie de nombreux ouvrages, principalement aux editions Actes Sud.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".