ADVANCING GERONTOLOGY THROUGH EXCEPTIONAL SCHOLARSHIP (AGES): FIRST INSIGHTS FROM A NEW MENTORSHIP MODEL
Bibliographic record
Abstract
Abstract Mentorship is vital to supporting professional growth among early career faculty members. Working with the Gerontological Society of America, the Advancing Gerontology through Exceptional Scholarship (AGES) Program was created as a mentorship model to promote productivity and peer support for early career faculty members. This presentation will: 1) describe the AGES Program as a prototype to facilitate peer support, collective learning, productivity, and co-authorship opportunities to advance early career faculty members; and 2) identify effective strategies to facilitate other groups looking to develop high-quality mentorship and training programs to support early career faculty members. After a competitive process, a cohort of 7 AGES members (including 2 program co-leads and 5 participants who all studied dementia) partook in monthly workshops from November 2022 to June 2023. Drawing on our experience with the AGES Program, we identified four strategies that cultivated our AGES Program: 1) being adaptable to address mentorship needs; 2) establishing accountability measures to enhance productivity; 3) fostering collective learning and peer support; and 4) delivering inspirational and educational activities. Following on the completion of the AGES Program, an open discussion was held with the cohort to assess and critically reflect on the program. Discussion results indicate the AGES Program was valuable in supporting early career faculty members. Specifically, findings demonstrate that the program was perceived as successfully supporting team science, collaborative opportunities, and enhancing productivity. Next steps are to conduct an anonymous evaluation survey to identify specific areas to strengthen and enhance the program for future cohorts.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".