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Record W4402161764 · doi:10.1093/jts/flae036

‘The Stamp of Their Own Iniquity’: Ottobah Cugoano on the Mark of Cain, Noah’s Curse, and Enslavers

2024· article· en· W4402161764 on OpenAlexaff
Jeremy Schipper

Bibliographic record

VenueThe Journal of Theological Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicColonialism, slavery, and trade
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurseWhite (mutation)Interpretation (philosophy)InterpreterArgument (complex analysis)HistoryReading (process)LiteratureGenealogyPhilosophySociologyArtAnthropologyLinguistics

Abstract

fetched live from OpenAlex

ABSTRACT Recently, Nyasha Junior has argued that interpretations of Cain by Black interpreters should not be understood as merely a reaction or corrective to anti-Black aetiological theories about the mark of Cain (Gen. 4:15). Surveying a range of 19th- and early 20th-century Black interpreters, Junior showed how they used the story of Cain to account for the violence that they believed is inherent in white people. In this article, I provide further evidence for this reading of Cain’s violence as an aetiological explanation for white violence. I analyse Ottobah Cugoano’s discussion of Cain in his Thoughts and Sentiments on the Evil and Wicked Traffic of the Slavery: and Commerce of the Human Species (1787) as a further example of this type of interpretation within African diasporic writings. In unmooring the mark from racial origins and linking it closely to racial violence, in part through a connection that Cugoano makes with Noah’s curse of his grandson Canaan in Genesis 9, elements of Cugoano’s argument anticipate the 19th- and early 20th-century Black interpreters who, as Junior discussed, argue that white people resemble Cain in their inherently violent tendencies.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.093
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.024
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.357
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2024
Admission routes1
Has abstractyes

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