Advancing Gerontology through Exceptional Scholarship (AGES): a Mentorship Initiative for Early Career Faculty
Bibliographic record
Abstract
Mentorship is critical to supporting professional development and growth of new and emerging faculty members. Working with the Gerontological Society of America (GSA), we created the Advancing Gerontology through Exceptional Scholarship (AGES) Initiative as a mentorship model to promote productivity and peer support for new and early career faculty members. In this commentary, we highlight the AGES Program as a prototype to facilitate peer support, collective learning, and co-authorship opportunities to advance new and early career faculty members, especially in the field of aging. Moreover, we identify four crucial strategies that cultivated and refined our AGES Program including: i) ensuring flexibility to address mentee needs; ii) establishing check-ins and accountability to enhance productivity; iii) fostering peer support and collective learning; and iv) delivering motivational and educational activities. Drawing on our experience with the AGES Program, this commentary provides recommendations to support other groups looking to develop high-quality mentorship programs to support new and early career faculty members in academia.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".