Trans-American Constructions of Black Masculinity: Dany Laferriere, le Negre, and the Late Capitalist American Racial machine-desirante
Bibliographic record
Abstract
Dany Laferrière's literary writings explode North American constructions of black masculinity, and in this paper, I explore how Laferrière configures le Nègre as an explosive dynamic within the late capitalist, American machine-désirante (“desiring machine”). As a Haitian-born writer who has lived in New York, Montréal, and Miami, as well as in Port-au-Prince and Petit-Goâve, Laferrière diasporizes constructions of black masculinity within trans-American landscapes. Splicing recent cultural criticism on black masculinity by African American scholars with theoretical writings by Gilles Deleuze and Félix Guattari on the “desiring machine,” this paper offers a re-reading of Laferrière's first and still most scandalous novel Comment faire l'amour avec un Nègre sans se fatiguer, focusing on the author's textual engagements with other black men--James Baldwin, Jean-Michel Basquiat, Miles Davis, Chester Himes, Spike Lee, Derek Walcott, Richard Wright, and Frantz Fanon, but especially Himes and Fanon--rather than his protagonist's sexploits with archetypal white women (Miz Littérature, Miz Beauté, Miz Suicide, and a coterie of others). Laferrière thus enters into the “sexual-textual” boxing ring of the American cultural imaginary: by engaging in ideological debate with other black male writers, Laferrière reveals how race-sex operates within the late capitalist, American “desiring machine” and shows how this operative mechanism can be exploited to jam the cultural machine.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".