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Record W4386458867 · doi:10.3389/fpsyg.2023.1139320

The intersection of race and femininity in the classroom

2023· article· en· W4386458867 on OpenAlexafffund
Naomi Faber, Monnica T. Williams

Bibliographic record

VenueFrontiers in Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsWhite (mutation)DeferenceRace (biology)PsychologyFemininityIntersection (aeronautics)DistressSocial psychologyGender studiesDevelopmental psychologySociologyPsychoanalysisClinical psychology

Abstract

fetched live from OpenAlex

This vignette told in eight graphic panels illustrates a story about how emotional responses associated with White femininity are used to derail a classroom discussion about racial injustice in a university setting. The panels show how this weaponization of femininity occurs and how it shields those who wield it from external criticism while centering themselves in conversations about race. Women of other races typically cannot access this psychological tactic, thus it constitutes a strategic intersectional use of race, psychology, and privilege to access a power position. In offering suggestions on how to respectfully engage in situations in which racial injustice is a topic of discussion, we unveil how failure of emotional regulation is part of the core psychological framework that leads to these kinds of power dynamics.

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)0.003
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0100.012
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.027
GPT teacher head0.411
Teacher spread0.384 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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