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Record W4388767001 · doi:10.1080/14788810.2023.2277859

What did Christopher Columbus once mean to Haiti? Émile Nau’s <i>Histoire des Caciques d’Haïti</i> and Irving’s <i>Columbus</i> in the nineteenth-century Atlantic World

2023· article· en· W4388767001 on OpenAlexafffund
Michael Reyes

Bibliographic record

VenueAtlantic Studies · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicCaribbean and African Literature and Culture
Canadian institutionsQueen's University
FundersAcadémie françaiseUniversity of TorontoUniversity of PennsylvaniaVanderbilt University
KeywordsScholarshipNationalismModernityLatin AmericansHistoryMileLong nineteenth centuryClassicsArt historyAnthropologySociologyAncient historyPolitical scienceLawGeography

Abstract

fetched live from OpenAlex

This essay responds to scholarship on nineteenth-century representations of Christopher Columbus, by reinscribing Haitian writing on Columbus into transnational approaches largely focused on the US and Latin America.Throughout this essay, I contend that Émile Nau's Histoire des caciques d'Haïti (1854) was profoundly influenced by Washington Irving's A History of the Life and Voyages of Christopher Columbus (1828).In particular, I show how Nau reframed Irving's text in nationalist terms to reimagine Columbus as the catalyst of modernity while asserting the centrality of Haiti to his life.To do so, I compare key passages in Nau and Irving to reconstitute Nau's enigmatic methodology and to show how Nau adapted Irving's text to inscribe Columbus within his vision of Haitian history that included the Spanish colonization of Hispaniola.This analysis reveals the ways in which Haitian cultural nationalism drew from and spoke back to the larger network of nineteenth-century Atlantic nationalisms.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0100.009
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.251
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2023
Admission routes2
Has abstractyes

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