Growing your own: Building research capability in higher education
Bibliographic record
Abstract
There is increasing pressure on universities to perform equally well in teaching and research. In Australia there will be demand for research leaders over the next decade in what is already a highly competitive environment. Whilst existing research leaders can be ‘planted’, there is untold possibility for universities ‘growing their own’ research leaders. However, little is known about what a successful career path might look like, or how universities can develop their own, let alone what the situation is like for women. This study sought to answer these questions by examining how the careers of the current generation of research leaders have been shaped. The study involved semi-structured biographical interviews and content analysis of the track records of 30 senior research leaders and administrators from a range of organisations across Australia and identified seven factors that contributed to their success. Based on these findings, a comprehensive program was developed and implemented to assist early career researchers (ECRs) develop a focused research career plan and build their track records. This study also examined comparative staff data by gender in research positions in Australian universities. Women currently hold almost half of the academic research-only positions and a third of deputy vicechancellor (DVC) roles with responsibility for the research portfolio, while comprising less than a quarter of the professoriate, and appear to be clustered at the lower levels in research-only positions. For the majority of Australia’s academic staff, the key to a successful and ongoing career is to learn how to successfully balance teaching and research, and how to manage the expectations of both deans and students. Future career development programs for ECRs should therefore recognise that managing both of those roles is now the reality of the working lives of the majority of academic staff in Australian universities. That is, any focus on research or teaching development programs should not be at the expense of skills in the other sphere, especially when academics are subject to cycles of governmental and policy change. This study provides a fledgling, but solid, evidence base upon which universities can design strategies to attract, retain, develop, and promote researchers, a priority that universities wishing to remain competitive cannot afford to ignore. Despite identifying avenues for further research to evaluate and extend the research in this study, the findings suggest that ‘growing your own’ is a possibility. More importantly, the study identifies ways to make growing the best a probability.
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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 | 0.070 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".