Women’s Access to Senior Management Positions in the University of Abuja – Nigeria
Bibliographic record
Abstract
Most of the research on women in higher education management has been conducted in the Westernsetting, particularly in the UK, USA, Canada and Australia. Similar studies have also been done inAsia, with reference to Thailand, Singapore, Hong Kong, and Malaysia, and in a few cases in Africa,with reference to South Africa and Kenya. Little has been done on the subject in Nigeria. In one of suchworks on a related issue, Oloruntoba & Ajayi (2006) used data on the research outputs of 219academics in three Nigerian agricultural universities to compare gender with research attainment. Thefindings showed that research attainment is slightly higher for male academics than for female, andacademic qualifications and rank are significantly associated with gender. The study also observedthat more male academic staff are employed at top management positions, while the majority of femaleacademic staff occupy middle management and entry levels. In another case, Nom, Onyeka &Jummai(2008) studied gender imbalance in access to higher education and employment in universities. Thestudy, based upon available data from the National Open University of Nigeria, found that genderimbalance existed in student enrolments and staff recruitment in the institution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".