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Record W4406215553 · doi:10.1037/cdp0000734

A mixed methods investigation of Indigenous university students’ experiences with and strategies to challenge racism.

2025· article· en· W4406215553 on OpenAlexaffabout
Iloradanon Efimoff, Katherine B. Starzyk

Bibliographic record

VenueCultural Diversity & Ethnic Minority Psychology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of ManitobaToronto Metropolitan University
Fundersnot available
KeywordsRacismIndigenousPsychologyRacial biasSocial psychologyGender studiesSociology

Abstract

fetched live from OpenAlex

OBJECTIVES: In this mixed methods program of research, we investigated Indigenous participants' experiences with racism at a Canadian postsecondary institution. METHOD: = 485), we surveyed Indigenous students about their experiences with racism. Participants responded to items about the frequency of potentially racist incidents, how those incidents made them feel, and if they considered those incidents as racist. They also responded to items about positive race-based experiences and their feelings about their on-campus experience. RESULTS: In Study 1, participants experienced many different types of racism: internalized (including racial microaggressions, modern racism, and old-fashioned racism), interpersonal, institutional, and structural. They also shared the negative impacts of experiencing racism and the ways they challenged and coped with racism. In Study 2, participants indicated that they experienced racism on campus regularly and that these experiences tended to make them feel bad. Participants also experienced positive race-based experiences and felt good in these cases. CONCLUSIONS: Anti-Indigenous racism happens with alarming regularity at the institution and negatively impacts Indigenous participants, though participants actively push back against racism. We discuss the implications and future research directions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
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 head0.084
GPT teacher head0.424
Teacher spread0.341 · 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 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
Published2025
Admission routes2
Has abstractyes

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