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The Self in Pain

2016· book-chapter· en· W2580517476 on OpenAlexaboutno aff
Andrea Stone

Bibliographic record

VenueUniversity Press of Florida eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsnot available
Fundersnot available
KeywordsPersonhoodNarrativeScholarshipGender studiesIdealismRomanceAestheticsColonialismAutonomyHistorySociologyLiteraturePolitical scienceArtPhilosophyLawEpistemology

Abstract

fetched live from OpenAlex

In stark contrast to the idealistic promotion of emigration and the ideal of the healthy self examined in chapter 2, the book turns from idealism to reality through the genre most prominently associated with mid-nineteenth-century Black American literature and which abolitionist Theodore Parker argued contained, “All the original romance of America.” Building on scholarship that complicates this notion of the quintessentially American nature of the genre, chapter 3 reads three women’s slave narratives, which demonstrate how epistemologies of health and tensions between colony and nation on the issue of black well-being frustrate projects of national definition and nation building. Rhetorics of health offer a new potential for the genre’s continued and deepening complex relevance. At focus are slave narratives of three formerly enslaved women in seldom-compared geopolitical and literary contexts: colonial Canada, the United States, and the West Indies. The narratives are Mary Prince’s (1831), Sarah Pooley’s (1856) and Lavina Wormeny’s (1861). These women’s rhetorics of health articulate notions of selfhood that challenge contemporary medical and legal definitions of humanness and personhood and produce radically promising alternatives to such categories and to the overall valorization of autonomy.

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 score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.013
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.013
GPT teacher head0.204
Teacher spread0.191 · 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
Published2016
Admission routes1
Has abstractyes

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Same venueUniversity Press of Florida eBooksSame topicRace, History, and American SocietyFrench-language works237,207