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Record W2990060071 · doi:10.1353/jer.2019.0084

Bodies in Motion: Liberian Settlers, Medicine, and Mobility in the Atlantic World

2019· article· en· W2990060071 on OpenAlexaboutno aff
Robert Murray

Bibliographic record

VenueJournal of the Early Republic · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Medicine and Tropical Health
Canadian institutionsnot available
Fundersnot available
KeywordsWhite (mutation)ResidenceColonialismAtlantic WorldInstitutionRace (biology)Gender studiesAtlantic slave tradeHistoryEthnologySociologyDemographyArchaeologyAncient historySocial science

Abstract

fetched live from OpenAlex

This article examines how movement between colonial Liberia and the United States shaped the construction of race through the experiences of one Liberian settler, Samuel F. McGill. In one of the great testaments to race as a social construction, the African neighbors and inhabitants of Liberia, who conceived of themselves as "black," recognized the significant cultural differences between themselves and the newly-arrived African Americans and racially categorized the newcomers as "white." There were significant ramifications for the settlers by becoming simultaneously white and black through their Atlantic mobility. The experiences of McGill highlight this racialized warping and shed light on the effects of movement in the Atlantic world between societies that constructed race differently. McGill is also historically significant as the first African American to receive a medical degree from an American institution, and his access to this education was dependent upon Atlantic mobility and his Liberian residence. McGill's medical education also provides a unique window to view the use of black cadavers for anatomical study and the logistics of the trade in these bodies among doctors and medical schools.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.251
Teacher spread0.215 · 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
Published2019
Admission routes1
Has abstractyes

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