TRAINING THE NEXT GENERATION OF GERONTOLOGICAL LEADERSHIP: WHO AND HOW?
Bibliographic record
Abstract
Abstract Knowing how to train the next generation of gerontological leaders involves understanding where we are now and where we want to be in the coming decades. We outline the results of a survey of the membership of Directors of Aging Centers. The Directors of Aging Centers interest group in GSA has representation from the USA, Canada, and Europe. A survey was sent to the membership in late December with reminders in January and had 31 respondents. We discuss the results of the survey, highlighting the demographics of the current leadership (Neil Charness), perceived need for training by current leaders (Peter Lichtenberg), and consensus content of leadership training programs (Patricia Heyn). Patricia D’Antonio provides a perspective on GSA’s approach to professional development programs and avenues for soliciting funding for leadership training. Our discussant (James Appleby, CEO of GSA) will reflect on the need for training in the context of building the next generation of gerontological leadership. We plan to devote significant symposium time to solicit audience input on next steps for supporting the effort to improve the quantity, quality, and diversity of the gerontological leadership workforce.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".