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Record W2126397683 · doi:10.1177/0894845314566943

The Glass Ceiling and Executive Careers

2015· article· en· W2126397683 on OpenAlexaffabout
Souha R. Ezzedeen, Marie‐Hélène Budworth, Susan D. Baker

Bibliographic record

VenueJournal of Career Development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsYork University
Fundersnot available
KeywordsGlass ceilingPsychologySocial psychologyBlameStereotype threatSituational ethicsStereotype (UML)Thematic analysisExecutive summaryWork (physics)SociologyQualitative researchPolitical science

Abstract

fetched live from OpenAlex

With respect to how the enduring challenge of the glass ceiling might be resolved, one position holds that parity in the executive ranks will be achieved, given enough women entering the managerial pipeline. However, there is scant evidence that such a pipeline exists, and pre-career women’s attitudes toward executive work remain to be better understood. Guided by theories of social role and stereotype threat, and research on work–life balance and culture, the study uses thematic discourse analysis to explore executive attitudes in an ethnically diverse sample of 69 Canadian undergraduate women in business. We find that they perceive the glass ceiling in stereotype threatening ways, blame their personal limitations and work–family choices for its existence, and sense a range of obstacles to their advancement. Although some expressed a desire for work–family balance, participants predominantly restricted career choices to favor one over the other. Implications, recommendations, and limitations are also discussed.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.267

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.014
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.152
GPT teacher head0.298
Teacher spread0.146 · 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 designObservational
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

Citations50
Published2015
Admission routes2
Has abstractyes

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