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Record W3205088322 · doi:10.1080/09518398.2021.1982047

Racial oases as spaces of positive racial identity socialization among African Canadian post-secondary students

2021· article· en· W3205088322 on OpenAlexaffabout
Beverly‐Jean Daniel

Bibliographic record

VenueInternational Journal of Qualitative Studies in Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRacismCognitive reframingSocializationIdentity (music)Coping (psychology)Gender studiesSociologyAfrican americanSocial psychologyPsychologyAnthropology

Abstract

fetched live from OpenAlex

This article focuses on the need identified by African Canadian students for a “racial oasis” – a physical space designed to increase their exposure to positive racial identities - which can support them in developing a community of support among peers who understand the effects of anti-Black racism, and to identify strategies for coping with racism. Research participants were drawn from a program developed to support African Canadian students navigate post-secondary schooling in Ontario, Canada. Participants indicated that safe spaces were central to developing a positive racial identity, and that these spaces provided opportunities for them to critically reframe their racialized identity. Participants also suggested that the development of a positive racial identity supports degree perseverance and educational pursuits. This research indicates that institutions must be intentional in providing the resources necessary to foster positive racial identity socailization amongst Black students and underscores the benefits of providing “racial oases” in schools, community organizations, and workplaces.

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)0.003
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.073
GPT teacher head0.571
Teacher spread0.498 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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