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Record W3088816961 · doi:10.1086/709976

A “tone of voice peculiar to New-England”

2020· article· en· W3088816961 on OpenAlexaboutno aff
Charmaine A. Nelson

Bibliographic record

VenueCurrent Anthropology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicColonialism, slavery, and trade
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismCONQUESTAfrican descentHistoryTone (literature)Resistance (ecology)PopulationEthnologyDiversity (politics)Ethnic groupGenealogySociologyAnthropologyLiteratureArtArchaeologyDemographyAncient history

Abstract

fetched live from OpenAlex

Found throughout the transatlantic world, fugitive slave advertisements demonstrate the ubiquity of African resistance to slavery. Besides noting things like names, accents, languages, and skills, they also recounted details that disclosed the regional origins and ethnicities of the runaways. Although detailed analysis of fugitive slave advertisements have been produced since the 1970s, Canadian slavery has been conspicuously absent from such studies. This article exposes and challenges Canada’s overwhelming absence from slavery studies more generally, recognizing the ways that the Underground Railroad has been enshrined in national curriculum and popular imagination to erase the colonial violence of Euro-Canadian settler histories. Challenging the erasure of Canadian slavery, fugitive slave advertisement will be analyzed to reveal the complex heterogeneity of the enslaved population of African descent. Focusing on Quebec from the moment of British conquest (1760), this article argues that this heterogeneity was a hallmark of the enslaved population of Quebec, which was composed of African Canadian, African American, African Caribbean, African-born, and indigenous enslaved peoples. The article then poses directions for future research that can further explore the cultural, linguistic, spiritual, and social implications of this extraordinary diversity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.076
GPT teacher head0.416
Teacher spread0.340 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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