MétaCan
Menu
Back to cohort
Record W3200563820 · doi:10.1093/alh/ajab062

Telling the Untold Story: Jewish Wartime Refuge in Haiti in Louis-Philippe Dalembert’s <i>Avant que les ombres s’effacent</i>

2021· article· en· W3200563820 on OpenAlexaff
Sarah Phillips Casteel

Bibliographic record

VenueAmerican Literary History · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicCaribbean and African Literature and Culture
Canadian institutionsCarleton University
Fundersnot available
KeywordsThe HolocaustJudaismCognitive reframingNarrativeHistoryRefugeeArt historyLiteratureArtPolitical scienceLawPsychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.559
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.196
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Literary HistorySame topicCaribbean and African Literature and CultureFrench-language works237,207