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Record W1434593027

Guilty Displeasures: White Resistance in the Social Justice Classroom

2014· article· en· W1434593027 on OpenAlexaff
Rakhi Ruparelia

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRacismWhite privilegeObligationWhite (mutation)Social psychologyResistance (ecology)BlameSociologyCriticismPrejudice (legal term)Context (archaeology)PsychologyPrivilege (computing)Gender studiesLawPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In this article, the author reflects on the challenges of teaching white law students about racism and white privilege as a racialized professor. To situate her experiences and to better understand the obstacles that professors who teach critically about race and racism confront, she draws from theories of racial identity development and research on student evaluations to contextualize student responses to anti-racist pedagogy. Grappling with racism in a meaningful way leaves many white students feeling distraught, angry and guilty, among other unpleasant emotions. Professors who initiate these discussions become the natural targets of criticism and blame as students struggle with their discomfort. The hostility of resistant white students can be interpreted as racial microaggressions that compromise the psychological well-being and deplete the emotional and physical resources of racialized professors. However, understanding negative student reactions in the context of structural racism and embracing students’ sense of disequilibrium as a necessary part of social transformation enable professors to reconceptualize personal attacks as something more constructive. The author concludes that teaching about racism and white privilege in a critical way, an obligation shared by all educators, offers personal and collective rewards that outweigh any costs.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.000
Research integrity0.0000.003
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 head0.033
GPT teacher head0.386
Teacher spread0.353 · 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.

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

Citations11
Published2014
Admission routes1
Has abstractyes

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