Building capacity in ageing research: Implications from a survey of emerging researchers in Australia
Bibliographic record
Abstract
Objective: The National Emerging Researchers in Ageing Study (NERAS) set out to inform capacity‐building efforts in ageing research. Its purpose was to identify the interest, attitudes and motives of PhD students to enter the field and factors influencing intention to remain. Method: A web‐based survey was sent to 267 PhD students in ageing. It assessed attitudes towards older people and the importance of a variety of factors influencing students’ interest and decision to engage in ageing research. Results: The response rate was 60% (n = 161). Positive attitudes, interest in ageing issues and concern for older people were key motivating factors to work or study in the field. Supervisors in ageing and initial interest in the field were key predictors of intention to remain in the field. Conclusions: NERAS is the first national study of emerging researchers in ageing and it provides important new knowledge with implications for capacity‐building efforts.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".