La «couleur noire» dans le panorama litteraire quebecois: la parodie de race et de sexe dans l’œuvre de Dany Laferriere
Bibliographic record
Abstract
Americans are obsessed with sex and fearful of black sexuality. The obsession has to do with a search for stimulation and meaning in a fast-paced, market-driven culture; the fear is rooted in visceral feelings about black bodies fuelled by sexual myths of black women and men. \nHooks argues that "images of black men as rapists, as dangerous menaces to society, have been sensational cultural currency for some time," further noting that "the obsessive media focus on these representations is political". In fact, the role such pervasive and deleterious images play in the maintenance of racist domination is to convince the public that black men are a dangerous threat who must be controlled by any means necessary, including annihilation. \nIn this paper, I address American constructions of black masculinity in Dany Laferrière's novels. His retheorizations of America and American identities are important to understanding his plays on stereotypes of black masculinity within the American racial "desiring machine", a term that I borrow from Deleuze and Guattari's A Thousand Plateaus. \nThis writer places his own narrative squarely within this vexed cultural, historical, capital, and racial nexus - in other words, at the heart of the racial problematic in America, even if Laferrière's representations of le nègre enter into stereotype precisely in order to parody, hyperbolize, and ultimately, pervert it.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".