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Record W2091869693 · doi:10.1080/13613324.2012.733688

White resentment in settler society

2012· article· en· W2091869693 on OpenAlexaff
Carol Schick

Bibliographic record

VenueRace Ethnicity and Education · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsResentmentWhite (mutation)PoliticsSociologyInclusion (mineral)NarrativeMulticulturalismGender studiesWhite supremacyGenerosityCriminologyRacismPolitical scienceLawPedagogy

Abstract

fetched live from OpenAlex

Teaching about the history and culture of aboriginal peoples in schools of white settler societies can serve as a counter to the dominant story that serves as the national narrative. Even though the actual teaching may well be among the least political and least disruptive type of curricular knowledge on offer, the inclusion of counter stories can meet with resistance and resentment. This article offers that the politics of resentment enacted in a white settler society complicates the potential for equitable schooling for aboriginal and racial minority students. Public acts of resentment on the part of white settler parents mark schools as white spaces even in the midst of claims of multicultural inclusion. The politics of resentment are used to normalize emotional belonging on the part of a white settler society that sees itself as beleaguered by its excessive generosity and inclusivity. This article examines the ways that white supremacy and white racial knowledge are reasserted through the effects of emotional belonging and resentment.

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)0.004
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 score0.017
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0170.023
Scholarly communication0.0060.003
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.035
GPT teacher head0.407
Teacher spread0.372 · 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

Citations40
Published2012
Admission routes1
Has abstractyes

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