Gender, professions and public policy: new directions
Bibliographic record
Abstract
Purpose This article aims to provide an overview on key trends in public sector policy and professional development and how they intersect with gender and diversity. It seeks to explore new configurations in the relationship between gender and the professions and to develop a matrix for the collection of articles presented in this volume. Design/methodology/approach The authors link social policy and governance approaches to the study of professions, using the health professions and academics as case studies. Material from a number of studies carried out by the authors together with published secondary sources provide the basis of our analysis; this is followed by an introduction of the scope and structure of this thematic issue. Findings The findings underline the significance of public policy as key to better understand gender and diversity in professional groups. The outline of major trends in public sector professions brings into focus both the persistence of gender inequality and the emergence of new lines of gendered divisions in the professions. Practical implications The research presented here highlights a need for new models of public sector management and professional development that are more sensitive to equality and diversity. Originality/value This article focuses on the “making” of inequality at the interface of public policy and professional action. It introduces a context sensitive approach that moves beyond equal opportunity policies and managerial accounts and highlights new directions in research and policy.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.018 | 0.037 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".