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Record W2090707589 · doi:10.1177/0002716211418445

Do Female Top Managers Help Women to Advance? A Panel Study Using EEO-1 Records

2011· article· en· W2090707589 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Annals of the American Academy of Political and Social Science · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsEqual employment opportunityWorkforceGender diversityCommissionDiversity (politics)Panel dataDemographic economicsBusinessWorkforce diversityFixed effects modelRepresentation (politics)Labour economicsPolitical scienceEconomicsEconomic growthFinanceCorporate governance

Abstract

fetched live from OpenAlex

The goal of this study is to examine whether women in the highest levels of firms’ management ranks help to reduce barriers to women’s advancement in the workplace. Using a panel of more than twenty thousand firms during 1990 to 2003 from the U.S. Equal Employment Opportunity Commission, the authors explore the influence of women in top management on subsequent female representation in lower-level managerial positions in U.S. firms. Key findings show that an increase in the share of female top managers is associated with subsequent increases in the share of women in midlevel management positions within firms, and this result is robust to controlling for firm size, workforce composition, federal contractor status, firm fixed effects, year fixed effects, and industry-specific trends. The authors also find that the positive influence of women in top leadership positions on managerial gender diversity diminishes over time, suggesting that women at the top play a positive but transitory role in women’s career advancement.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.365
GPT teacher head0.432
Teacher spread0.067 · 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