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Record W4386863273 · doi:10.1002/ejsp.2987

The impact of education about historical and current injustices, individual racism and systemic racism on anti‐Indigenous racism

2023· article· en· W4386863273 on OpenAlexafffundabout
Iloradanon Efimoff, Katherine B. Starzyk

Bibliographic record

VenueEuropean Journal of Social Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of ManitobaToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Manitoba
KeywordsRacismIndigenousPrejudice (legal term)Psychometrics of racismFeelingSociologyGender studiesPolitical sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract Anti‐Indigenous racism is a pressing issue in Canada. Education on historical and contemporary Indigenous topics is a common strategy to challenge such racism. Despite the existence of education‐based programmes intended to address anti‐Indigenous racism, there is limited evidence that they are effective. To this end, we report the results of two longitudinal experiments ( N 1 = 639, N 2 = 1099) assessing the effect of education on anti‐Indigenous racism. In both studies, we assessed the impact of five conditions on Indigenous‐related outcomes in samples of non‐Indigenous students. All experimental conditions included information on historical and current injustices toward Indigenous people. In some conditions, we added content on individual racism, systemic racism, or both individual and systemic racism. Results indicated that the experimental conditions improved Indigenous‐related thoughts, feelings, knowledge and behaviours. Further, the conditions containing systemic racism content outperformed the other conditions. These studies showcase the potential of education to reduce anti‐Indigenous prejudice.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.047
GPT teacher head0.444
Teacher spread0.397 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
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
Published2023
Admission routes3
Has abstractyes

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