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Record W1859200856 · doi:10.47678/cjhe.v34i1.183445

Gender as a Barrier for Women With Children in Academe

2004· article· en· W1859200856 on OpenAlexfundvenueaboutno aff
Carmen Armenti

Bibliographic record

VenueCanadian Journal of Higher Education · 2004
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
FundersCanadian Federation of University Women
KeywordsCrunchDenialRestructuringPromotion (chess)Higher educationPsychologyCareer developmentBaby boomersSociologySocial psychologyPolitical scienceDemographic economicsLawMedicine

Abstract

fetched live from OpenAlex

This research involved in-depth interviews with nineteen women professors, drawn from across various faculties and ranks at one Canadian university, and was intended to explore the interconnections between the women's personal and professional lives. The women in this study chose to combine having children with an academic career. Most of them depicted their career trajectory as a lifelong challenge, one that was both fulfilling and prestigious. In contrast, the women reported a number of obstacles in their career paths that served to prevent them from gaining full membership in academic life. This study probes the nature of such obstacles that are grouped into two categories: the child-related time crunch and the career-related time crunch. As a result of these obstacles, the women encountered childbearing/childrearing problems, research dilemmas, a willingness to leave the academy, and denial of tenure and promotion. Findings call for a restructuring of academic careers in order to effectively accommodate women with children in the profession.

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)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0200.007
Scholarly communication0.0060.002
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.000

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 head0.022
GPT teacher head0.316
Teacher spread0.293 · 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 designQualitative
DomainIncentives
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

Citations66
Published2004
Admission routes3
Has abstractyes

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