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Record W4323022194 · doi:10.21423/awlj-v37.a9

Literature Review of The University Teaching Trap of Academic Women

2017· article· en· W4323022194 on OpenAlexaboutno aff
Helene A. Cummins

Bibliographic record

VenueAdvancing Women in Leadership Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsNeglectWork (physics)SociologyHigher educationPublic relationsBalance (ability)Political scienceSocial workCareer developmentTrap (plumbing)Service (business)Medical educationPedagogyPsychologyBusinessMedicineEngineeringMarketing

Abstract

fetched live from OpenAlex

Commonplace division of labor practices in Canadian academia favor a forty percent focus on each of teaching and research, with a twenty percent focus on service. The social climate, workplace culture, and social structure of academe often burdens women faculty with excessive teaching responsibilities. This may inhibit both their career success, and personal work-life balance. The absence of workplace policies, career and institutional support often encumbers women faculty and produces inequities in the workplace triggering what is defined as the "teaching trap". Smaller universities, financial cutbacks in the university system, and the general neglect of the needs of women academics serves to maintain both unfair, and unequal treatment of women scholars in the academy. The implications of these issues are discussed.Keywords: Women faculty, university teaching trap, workplace culture, structural barriers, Canada

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)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.687

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0200.044
Science and technology studies0.0040.003
Scholarly communication0.0050.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.121
GPT teacher head0.330
Teacher spread0.209 · 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.

Study designNot applicable
DomainIncentives
GenreReview

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

Citations6
Published2017
Admission routes1
Has abstractyes

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