The glass ceiling in the 21st century:understanding barriers to gender equality
Bibliographic record
Abstract
"Since the term glass ceiling was first coined, women have made great progress in achieving leadership equality with men in the workplace. Despite this, women are still underrepresented in the upper echelons of organizations. In this volume, leading psychologists from the United States, Canada, and the European Union go beyond social commentary, anecdotal evidence, and raw statistics to explain and offer remedies, based on empirical evidence, for this persisting inequality. Subtle barriers to women's advancement to and success in leadership positions are a major focus; for example, women are regularly recruited for upper-level positions that are associated with a high risk of failure, and female managers are stereotyped as either competent or warm--but not both. Other obstacles associated with encountering or breaking through the glass ceiling include more nuanced forms of gender stereotyping, tokenism, and sexual harassment. The somewhat surprising effects of affirmative action and family-friendly policies are also examined. The editors and contributors to this volume offer a range of practical solutions at the level of the organization (e.g., affirmative action), the work group (e.g., diversity management), and the individual (e.g., cross-cultural networking). They offer women viable suggestions for making career choices and for thriving in the hard-won positions they have attained"--Jacket. (PsycINFO Database Record (c) 2009 APA, all rights reserved)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".