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Record W4306968918 · doi:10.1080/23793406.2022.2136106

Whiteness and damage in the education classroom

2022· article· en· W4306968918 on OpenAlexaffabout
Alexandre Emboaba Da Costa

Bibliographic record

VenueWhiteness and Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComplicityRacismWhite (mutation)OppressionAgency (philosophy)IndigenousSociologyColonialismConsciousnessGender studiesWhite supremacyCriminologyPolitical sciencePsychologyPoliticsLawSocial science

Abstract

fetched live from OpenAlex

This paper analyses relationships between whiteness and damage in the university classroom through a focus on two contemporary areas of critical education in Canada: raising white racial consciousness and truth and reconciliation between Indigenous and non-Indigenous people. First, whiteness is damage-producing – it orients anti-racist education towards white students and their needs, there by harming the well-being and constraining the education of non-white students. Second, whiteness gravitates towards what Unangax scholar Eve Tuck calls “damage-centred approaches,” which objectify non-white suffering, pathologising Indigenous peoples whilst obfuscating the ongoing reproduction of racism and colonialism. As such, white educators must remain assiduously vigilant about a key tension regarding whiteness and damage: that our pedagogical focus on racial and colonial oppression can simultaneously raise critical consciousness and divert attention away from more fundamental interrogations of whiteness, agency, and relationality within a systemically racist social order. The article closes with some considerations for educators in terms of addressing complicity in their institutions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.343
Teacher spread0.331 · 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 teacher head, 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

Citations1
Published2022
Admission routes2
Has abstractyes

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