Bibliographic record
Abstract
Despite the many changes in the organisation of work since the early 1980s, women continue to face a range of obstacles in their careers and career opportunities (Adkins, 1995; Dale, 1990). It is relatively easy to demonstrate areas of work where women have achieved success (White, Cox, & Cooper, 1992), for example in higher education, with several women now holding Vice Chancellorships of Universities, and in finance and accountancy where women appear to be able to achieve board-level appointments. However, it is extremely difficult to assess whether women's route to ‘success’ is different to men's and, if it is, how that difference is experienced by women. Considering the defining features of ‘successful’ women identified by some research (for instance, see Nicholson & West 1988; White et al ., 1992) as, relative to their male counterparts, more likely to be single or childless or to have children late, it seems likely that women's route to ‘success’ is somewhat different to that of men's. These factors, of course, not only differentiate ‘successful’ women from ‘successful’ men but also from less ‘successful’ women. Drawing on the literature of gender and work, this chapter will argue that, in part, this is due to the fact that the notion of career ‘success’ is traditionally defined in what are male terms. The chapter examines the issue of power in the work place and its implications for women's career and considers what contribution a feminist understanding of career might make to the future of work and social arrangements.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".