Women leading research in Australian universities : are we there yet?
Bibliographic record
Abstract
While data on the representation of academic women in Australian universities has been monitored since the mid-1980s, little is known about how their representation has changed since a study undertaken in the mid-2000s (Winchester, Lorenzo, Browning, & Chesterman, 2006). To determine how academic women are currently represented in Australian universities, and particularly in research positions, we examined the most recent comparative data by academic level over time, and in research positions. We found that the increase in representation of women in academic positions in Australian universities was dramatic between 1985 and 2005. It has been slower but consistent since then, to the level where minimal further change is expected as parity has almost been achieved. Women currently hold almost half of the academic research-only positions and a third of deputy vice-chancellor (DVC) roles with responsibility for the research portfolio, while comprising less than a quarter of the professoriate. Although representation has been on an upward trajectory, closer examination of the data indicates that women may be clustered at the lower levels in research-only positions, in the same way as all academic women were clustered at the junior levels almost three decades ago. Our study of women in research is particularly timely as the academic workforce in universities is dominated by baby-boomers who will eventually retire, and women will, therefore, have increased opportunities over the next 10-20 years to take up those positions inevitably vacated by the baby-boomers.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".