MétaCan
Menu
Back to cohort
Record W4414515548 · doi:10.1017/s0008423925100735

Six Pipelines: Invigorating Race in Canadian Political Science

2025· article· en· W4414515548 on OpenAlex
Seon Yuzyk

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Political Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Science Research and Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRace (biology)Diversity (politics)PoliticsRacismReading (process)Curriculum

Abstract

fetched live from OpenAlex

Abstract This study examines the absence and presence of race- and anti-Black-related issues in Canadian political science. This research employs a six-pronged mixed methods approach, combining quantitative data analysis with qualitative examinations of race debates within the discipline. It investigates introductory textbooks, Black Studies programs, graduate courses, comprehensive examination reading lists, the Canadian Journal of Political Science and academic awards. The findings reveal that Canadianists are not exempt from the effects of racism. The results highlight significant challenges in decolonizing Canadian political science, such as incorporating race into university curriculum and providing diversity training for editorial committees at major academic presses. This study underscores the pervasive reach of racism and anti-Blackness in the country and calls for adopting relational approaches to studying Black people in Canada. It contributes to the growing discourse on anti-Blackness, addressing crucial gaps in the discipline.

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.

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)0.020
metaresearch head score (Gemma)0.076
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.076
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0020.013
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.407
Teacher spread0.375 · 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