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Record W1977646341 · doi:10.1080/09540250701295460

Women ‘learning to labour’ in the ‘male emporium’: exploring gendered work in teacher education

2007· article· en· W1977646341 on OpenAlexaffabout
Sandra Acker, Jo‐Anne Dillabough

Bibliographic record

VenueGender and Education · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
FundersUniversity of California, Davis
KeywordsWork (physics)SociologyGender studiesPedagogyPsychology

Abstract

fetched live from OpenAlex

This article reflects an interest in exposing links between women's academic work and the gender codes which organize and shape working life in the university context, both now and in the recent past, as a contribution to the sociology of women's work. Our specific focus is the gendered division of labour in teacher education in universities in Ontario, Canada. Drawing on a theoretical framework based on Bourdieu and McNay, and through an analysis of semi‐structured interviews with 19 women who worked in faculties of education between the 1960s and 1990s, we examine how the gendered division of labour has influenced the careers and working lives of women university teacher educators during those decades. Our data are organized under three themes: public and private lives; women's work/place; and talking back. We identify continuities and changes as well as qualifiers, ironies and paradoxes.

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)0.005
metaresearch head score (Gemma)0.005
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 score0.135
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0200.035
Scholarly communication0.0100.006
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.217
GPT teacher head0.396
Teacher spread0.179 · 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

Citations133
Published2007
Admission routes2
Has abstractyes

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