Bibliographic record
Abstract
Purpose This collection seeks to examine the various challenges women face in advancing their careers. Design/methodology/approach In the mid‐1980s, the phrase “glass ceiling” was coined and has since become an established part of our vocabulary. The glass ceiling refers to an invisible but impermeable barrier that limits the career advancement of women. During the last two decades, women have made progress: there are now more women in senior‐level executive jobs, more women in “clout jobs”, more women CEOs, and more women on corporate boards of directors. But real progress has been slow with only modest increases shown at these levels. Findings The slow progress made by talented, educated, ambitious women is now having some negative effects on women's views of management and the professions as a career. However, artificially limiting the career possibilities of women is a luxury organizations can no longer afford. Organizations are facing an impending shortage of qualified leaders. The aging of the workforce, a smaller number of new workforce entrants, and the war for talent, makes it imperative that organizations utilize and develop the talents of all their employees. Originality/value This collection examines the various challenges women face in their careers. The contributors come from a number of different countries, indicating the widespread interest in this topic in all developed and developing countries.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".