Bibliographic record
Abstract
Alexandre Dumas s’engagea avec fougue dans des combats politiques et artistiques. Pourtant, celui qui était le petit-fils d’une esclave haïtienne et qui fut la victime de préjugés racistes tout au long de sa carrière littéraire choisit de s’abstenir pour la cause abolitionniste. Préférant se faire valoir en tant que Français plutôt que métis, Dumas peupla ses romans et récits de voyages de personnages de couleur, certains suscitant l’admiration, d’autres perpétuant les pires stéréotypes. D’aucuns ont vu dans son œuvre un complexe mélanique, un auto-blanchissement, un désaveu de ses origines. En prenant en compte la manière dont Dumas raconte ses ancêtres et s’écrit lui-même, puis son utilisation de concepts scientifiques et de préjugés racistes, cet essai examine la stratégie de l’auteur, sa volonté de donner une humanité aux figures d’altérité et d’éduquer ses lecteurs.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".