Bibliographic record
Abstract
This chapter surveys the history of Haitian cinema and the challenges that have prevented it from attaining the size of other national cinemas. It also discusses films related to the Haitian Revolution which were made by Haitians. These include documentaries and dramas debating the Revolution’s legacy (refracted either through the Duvalier era, the Bicentennial/Aristide era, or the 2010 earthquake), or celebrating revolutionary heroes from Toussaint Louverture to Catherine Flon. Though these films are less well-known than foreign films on the Revolution, they offer some of the best cinematic treatments of this event. The directors treated include Haiti’s most prominent filmmakers (Arnold Antonin and Raoul Peck), as well as diaspora filmmakers from Miami to Canada. The chapter also analyzes collaborations involving Haitian intellectuals working with French, Cuban, or North American directors. Haitians involved in such collaborations include Wyclef Jean, Lyonel Trouillot, Frantz Voltaire, Ralph Maingrette, and Dany Laferrière.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".