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Record W2783045999 · doi:10.1177/0021934717749417

Different Tales of John Glasgow: John Brown’s Evolution to <i>Slave Life in Georgia</i>

2018· article· en· W2783045999 on OpenAlexaboutno aff
Ikuko Asaka

Bibliographic record

VenueJournal of Black Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicColonialism, slavery, and trade
Canadian institutionsnot available
Fundersnot available
KeywordsArtHistorySociologyArt history

Abstract

fetched live from OpenAlex

This article seeks to advance conversation on the literary and political agency of fugitive slave narrators and their far-reaching archival footprints by focusing on the evolution of John Brown’s narrative of John Glasgow, a Demerara-born free Black sailor with whom Brown toiled side by side on a Georgian plantation. In British and U.S. abolitionist discourse, Glasgow’s tragic story—he was imprisoned under Georgia’s seamen law upon arriving in Savannah and eventually fell into bondage—made him the symbol of the southern seamen acts’ egregious infringement of British freedom. Brown, a formerly enslaved expatriate resident in England, told this tale in his autobiography Slave Life in Georgia, but the authorship of this story has some ambiguity. It is believed by some scholars that the narrative’s editor, London-based White abolitionist Louis Alexis Chamerovzow, concocted the tale. By drawing on newly discovered documents, this article demonstrates that Brown originally attributed Glasgow’s enslavement to kidnapping by deceit, not to a Black seamen law. Furthermore, an examination of British diplomatic dispatches and the details of the Black seaman law operating in Savannah at that time posits the likelihood that Glasgow became enslaved by deception rather than law. What do we make of these findings? Instead of marshalling them to confirm Chamerovzow as the story’s creator, this article speculates that John Brown himself invented the Glasgow story and imagines a transatlantic Black political circuitry connecting England and Canada.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.334
Teacher spread0.295 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2018
Admission routes1
Has abstractyes

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