MétaCan
Menu
Back to cohort
Record W3034683231 · doi:10.1017/s0008423920000207

Whiteness, Power and the Politics of Demographics in the Governance of the Canadian Academy

2020· article· en· W3034683231 on OpenAlexaffabout
Genevieve Johnson, Robert B. Howsam

Bibliographic record

VenueCanadian Journal of Political Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRacializationPoliticsGender studiesCorporate governanceRepresentation (politics)Diversity (politics)White (mutation)Power (physics)TokenismFace (sociological concept)DemographicsSociologyPolitical scienceIntersectionalityPublic administrationRace (biology)Social scienceManagementLawDemography

Abstract

fetched live from OpenAlex

Abstract The predominance of Whiteness, and the corresponding lack of representation of people who are both racialized and minoritized, in the governance of universities is a political issue. We present the results from an intersectional diversity audit of central and senior academic administrators at five Canadian universities: Simon Fraser University, University of British Columbia, University of Toronto, University of Victoria and York University. Our findings indicate that racialized men and women are hitting ceilings in the middle administrative ranks. Conversely, we find a notable overrepresentation of White men and women in the senior administrative ranks. Our analysis suggests that White women, unlike racialized women and men, no longer face serious barriers to representation within these senior ranks. These findings raise concerns about processes of racialization that may impede career progress for some but accelerate it for others. They raise concerns about the politics of who lifts whom into the echelons of academic decision making, which in turn has implications for justice, knowledge and social meanings of competency.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.008
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.066
GPT teacher head0.283
Teacher spread0.217 · 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 designObservational
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

Citations26
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Political ScienceSame topicGender Diversity and InequalityFrench-language works237,207