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Record W4382457278 · doi:10.53967/cje-rce.5905

Chilly Climate 2.0: Women’s Experiences of Harassment and Discrimination in Canadian Higher Education

2023· article· en· W4382457278 on OpenAlexaffvenueabout
Janette Hughes, Hannah Scott, Laura Morrison, Donna Kotsopoulos, Robyn Ruttenberg-Rozen

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsWestern UniversityOntario Tech University
Fundersnot available
KeywordsHarassmentCovertExcellenceSilenceHigher educationGender equityEmpowermentEquity (law)Inclusion (mineral)Public relationsSociologyPsychologyPolitical scienceGender studiesSocial psychologyLaw

Abstract

fetched live from OpenAlex

This research examines the extent to which issues identified in Breaking Anonymity (The Chilly Collective, 1995) are still salient despite new EDI mandates/programs which support increased research excellence through EDI principles. We present survey results for Canadian academics who identify as women (n = 244) regarding their experiences with gender-based harassment and discrimination. Our analysis identified three categories of patriarchal gendered control: (1) overt practices, (2) covert practices, and (3) a systematic effort to silence the reporting of these experiences. We highlight the voices of women academics as they provide personal insights into the continuing barriers through their experiences. Through their stories, the implications of existing overt and covert harassment and discrimination practices are discussed. Our study provides an overview of women academics’ experiences with oppression by their male colleagues and contributes to research exploring equity and inclusion in higher education and the continued need to work toward gender equity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0540.019
Scholarly communication0.0100.003
Open science0.0020.010
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.093
GPT teacher head0.319
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2023
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Education / Revue canadienne de l éducationSame topicGender Diversity and InequalityFrench-language works237,207