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Record W2092822807 · doi:10.1525/jlin.2001.11.1.65

See No Evil, Speak No Evil: White Police Officers' Talk about Race and Affirmative Action

2001· article· en· W2092822807 on OpenAlexaff
Bonnie McElhinny

Bibliographic record

VenueJournal of Linguistic Anthropology · 2001
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAffirmative actionWhite (mutation)Race (biology)OfficerSociologyIdeologyRacismCriminologyAction (physics)White supremacyLawGender studiesPolitical sciencePolitics

Abstract

fetched live from OpenAlex

This article analyzes three discursive strategies which White police officers use to talk about affirmative action. In different ways, these strategies allow officers to claim to see no racial difference or inequity. In one instance, however, a White officer did remark upon her own Whiteness in terms of cultural difference. I consider the implications of this fact for recent debates in anthropology about the relationship of culture and ideology, as well as for further studies of Whiteness.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0140.018
Scholarly communication0.0080.008
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.048
GPT teacher head0.331
Teacher spread0.283 · 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 designQualitative
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

Citations30
Published2001
Admission routes1
Has abstractyes

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