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Record W2961588875 · doi:10.1111/soin.12281

When Rape Was Legal—The Untold History of Sexual Violence During Slavery by Rachel A.Feinstein. 2018. Routledge, Taylor and Francis Group: New York, NY. 108 pp. $29.57 paper. ISBN: 9781138629684.

2019· article· en· W2961588875 on OpenAlexaff
Daniel Sailofsky

Bibliographic record

VenueSociological Inquiry · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsMcGill University
Fundersnot available
KeywordsSociologySexual violenceMedia studiesCriminology

Abstract

fetched live from OpenAlex

Book Review of When Rape Was Legal—The Untold History of Sexual Violence During Slavery by Rachel A. Feinstein \n \nIn When Rape Was Legal—The Untold History of Sexual Violence During Slavery, Rachel A. Feinstein writes clearly and succinctly on the often ignored and dismissed history of sexual violence against black women during slavery in the American South. More than just a historical account of the systematic sexual exploitation of these women, this book builds on Collins’ (2005) matrix of domination by examining the larger consequences of this history in shaping the intersectional gendered racist system of oppression that persists today. Though reductive at times, this analysis provides a glimpse into the layered nature of intersectional oppression, forcing us to consider how modern-day white privilege and power are still in part shaped by the sexual violence and exploitation of black women during slavery. [...]

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)0.002
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.002

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 head0.059
GPT teacher head0.277
Teacher spread0.218 · 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
GenreReview

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
Published2019
Admission routes1
Has abstractyes

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