Telling the Untold Story: Jewish Wartime Refuge in Haiti in Louis-Philippe Dalembert’s <i>Avant que les ombres s’effacent</i>
Bibliographic record
Abstract
Abstract Literary narratives of Jewish refugees in the Caribbean uncover a forgotten chapter of wartime history. A key example is Haitian author Louis-Philippe Dalembert’s novel Avant que les ombres s’effacent (2017), which tracks the traumatic dispersion of a Polish Jewish family to Haiti, Cuba, Israel, and the US in the late 1930s. Dalembert interweaves the tale of his Jewish protagonist’s flight to Haiti with portrayals of the Haitian émigré community in Paris and the Haitian concentration camp prisoner Jean-Marcel Nicolas. Blending fact and fiction, the novel highlights the Haitian state’s little-known efforts to aid Holocaust refugees and connects those efforts to the island nation’s own revolutionary history. In this article, I argue that fiction’s unique traits as a medium of cultural memory enable Dalembert to reframe the wartime past from a Haitian perspective. Avant que les ombres s’effacent harnesses the fictional privileges of literary narrative, mediating between the real and the imaginary and combining Jewish and Caribbean memory systems in unexpected and often startling ways. Generating images of the wartime past that transform our perception of it, the novel moves Haiti to the center of the story and in so doing uncovers global dimensions of Jewish experience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".