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Record W2070270253 · doi:10.1080/13613320802478838

Whiteness in/and education

2008· article· en· W2070270253 on OpenAlexaffabout
Dominique Rivière

Bibliographic record

VenueRace Ethnicity and Education · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsInstitute for Christian StudiesUniversity of TorontoToronto Rehabilitation Institute
Fundersnot available
KeywordsSociologyCritical theoryRacial biasHigher educationPedagogyGender studiesSocial scienceRacismPolitical scienceLaw

Abstract

fetched live from OpenAlex

This article discusses my interactions with the teacher in whose classroom I conducted my doctoral research. That project was concerned with using transformative Drama pedagogy to reconceptualise cultural identity in multicultural curricular policy. The participants in my study comprised of 15 Grade Nine Drama students and their teacher at ‘May Valley High School’, located just outside of Toronto, Canada. During the Fall semester of the 2004–2005 academic year, I observed the students’ performances (both in and out of dramatic role) of their gender, sexuality, racial and ethnic identities. Using a performative lens to analyse the connections between their ‘fictional’ identity performances, and their ‘actual’ ones, I showed how, in this particular classroom, those connections often served to reinforce rather than challenge hegemonic constructions of social identity and identification. I suspected that this had much to do with their teacher’s pedagogical orientations and practices, with respect to multiculturalism and ethno‐cultural difference. In this article, I tease out how the ‘Whiteness’ embedded in his pedagogy served to perpetuate institutional racism at the school.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.015
Scholarly communication0.0070.003
Open science0.0000.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.031
GPT teacher head0.381
Teacher spread0.350 · 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

Citations48
Published2008
Admission routes2
Has abstractyes

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