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Record W2978800542 · doi:10.20381/ruor-23937

Microaggressions: Black Students' Experiences of Racism on Campus

2019· dissertation· en· W2978800542 on OpenAlexaboutno aff
Ejiro Agbaire

Bibliographic record

VenueuO Research (University of Ottawa) · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRacismSociologyGender studiesPsychology

Abstract

fetched live from OpenAlex

This thesis is based on three different focus groups held in the summer of 2018 with a total of twelve Black students. It examines a group of Black students’ experiences of racist microaggressions on the campus of a large comprehensive Canadian university situated in an urban setting. Using Critical Race Theory it analyzes how seemingly neutral comments, slights, snubs or representations by white students and professors contributes to a culture of anti-Black racism on this campus. Key to this analysis is the shift from traditional forms of racism to more subtle forms of racism in contemporary society, and the role that institutions play in reproducing racism. Microaggressions thus characterise the subtle way in which racism is perpetuated in contemporary society. The experiences described by the twelve students in this research study demonstrate the prevalence of microaggressions in the lives of Black students in this Canadian university. Furthermore, the four broad themes emerge from the focus group discussions: the lack of diversity in the student population and faculty, the invalidation of Black experiences, stereotypical representations of Black people and cultures, and gendered racism, give further nuance to the types of messages that Black students are exposed to at this university. This analysis produces a deeper understanding of how these micro-level interactions contribute to the broader culture of racism on campuses.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.442
Teacher spread0.396 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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