Bibliographic record
Abstract
Dans son premier roman, Comment faire l’amour avec un Nègre sans se fatiguer, Dany Laferrière déploie un discours antiraciste et anticolonialiste rendu possible à la fois par l’effacement narratif des femmes noires et par l’exploitation de stéréotypes sexistes à propos des femmes blanches. Alors qu’il remet en question au moyen de l’intertextualité et de l’ironie, notamment, les stéréotypes touchant le « Nègre », Laferrière reconduit ceux qui touchent la Blanche ; en fait, ce sont ces derniers qui permettent la mise en oeuvre de son discours antiraciste, comme si le racisme justifiait le sexisme. Il en découle une déshumanisation de la femme blanche, un effacement de sa subjectivité qui permet l’affirmation de celle du narrateur. Or, si le racisme est problématique, le sexisme ne l’est pas moins, et il importe de le rappeler.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".