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Record W7095905629

BOOK REVIEW Title: Whose University Is It, Anyway? Power and Privilege on Gendered Terrain

2016· article· en· W7095905629 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Theory and Gender Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPrivilege (computing)Power (physics)QueerIdentity (music)NarrativeSection (typography)Situational ethicsTerrain
DOInot available

Abstract

fetched live from OpenAlex

As a visible minority educator, I am always interested in analyzing the personal tensions, environmental barriers, and imaginative possibilities that exist when embracing equity- and diversity-conscious approaches to teaching, learning, and interacting in higher education institutions. In Whose University Is It, Anyway? Power and Privilege on Gendered Terrain, these areas of tension are treated as creative spaces to draw from the work of feminists who use this site as a site for power and new knowledge production. Whose University Is It, Anyway? Power and Privilege on Gendered Terrain motivates the reader to question whether equity is available to all individuals whose identity is interwoven with gender, race, ethnicity, disability, social class, and religion and within various situational subjects (e.g., student, teaching assistants, faculty, and administrators) in a Canadian context. The book has four parts; the first section illustrates the challenges facing racialized minority women, Aboriginal women, and women with disabilities. The second section explores various experiences such as those related to racialized minority scholars, and women who have experienced violence, queer and gendered individuals. The third section provides narratives of women in various academic roles (teaching assistants, administrative assistants, department chairs, and non-tenure-track faculty). The diversity of the

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)0.000
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.078
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.001

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 head0.028
GPT teacher head0.296
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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