Breaking Through the Glass Ceiling: Strategies to Enhance the Advancement of Women in Ghana’s Public Service
Bibliographic record
Abstract
Ghanaian women have made, and continue to make, considerable progress on their journey to the upper echelons of the decision-making institutions of the country. However, the overall number of women in decision-making positions, especially in the civil service, is distressingly small. At the end of 2011, for example, of 36 positions available only six were filled by women, as chief directors of a ministry. What is being witnessed in the civil service, then, is what has been described in the academic literature and popular press as the glass ceiling. This paper examines what has been and is being done by government, and what sort of strategies will be necessary to deal with the problem. The questions addressed are what are the strategies; and how effective are they in breaking down the glass ceiling that appears to exist in the civil service and which prevents women from progressing into senior management. What is the way forward – or up – in breaking through the glass ceiling?
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".