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Record W2991620119 · doi:10.1080/13562517.2019.1696296

Mind the (gender) gap: engaging students as partners to promote gender equity in higher education

2019· article· en· W2991620119 on OpenAlexaff
Anita Acai, Lucy Mercer‐Mapstone, Rachel Guitman

Bibliographic record

VenueTeaching in Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScholarshipEquity (law)Higher educationGender equityAgency (philosophy)SociologyPublic relationsGender studiesFeminismGender gapPedagogyPolitical sciencePsychologySocial scienceDemographic economics

Abstract

fetched live from OpenAlex

Gender inequity remains a critical issue in higher education. We explored the proposition that engaging students as partners (SaP), an increasingly adopted approach to student engagement, may present one approach to improving gender equity by fostering agency and leadership for women. First, we analyzed the gender distribution of authors of SaP scholarship spanning 202 articles published in six academic journals over the past five years. Women were more likely to author (70%) and lead (76%) SaP publications. Second, we used collaborative autoethnography to explore our experiences as three women SaP practitioners. Affirmative partnerships built our agency to assert our voices and empowered us to advocate for gender equity. These data indicate that SaP may present one approach to promoting gender equity by creating ‘brave spaces’ and ‘sites of resistance’ against gendered norms in academia.

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)0.013
metaresearch head score (Gemma)0.019
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 score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0090.006
Open science0.0010.015
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.224
GPT teacher head0.519
Teacher spread0.294 · 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

Citations28
Published2019
Admission routes1
Has abstractyes

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