Academic leadership careers are not limited to scientific research
Bibliographic record
Abstract
Early career researchers (ECRs) are often faced with uncertainty about their professional futures, a challenge exacerbated by the increasing pressures within the academic research landscape. As ECRs navigate their next steps in science, mentorship is crucial, particularly as they face points of decision-making and possible career diversions from the traditional postdoctoral-to-professor pathway. In response to these challenges, the second iteration of the Australian and New Zealand Society of Immunology (ASI) Mentor-Mentee Program aimed to provide mentorship and training to ECRs about academic career pathways in research and education to bridge the professional communities, values and advice of these two often independent career choices. As a component of the program, three eminent Australian immunologists in research-intensive, teaching and research (TnR) and education-focused careers shared their professional journeys and experiences which led to their chosen career pathways in national workshops. Here, we share their insights, lessons learned and professional development tips to establish an academic career. By outlining the three primary scientific academic career pathways available post-PhD, we aim to inform and inspire the next generation of immunologists as they consider the diversity of possible academic careers ahead.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".