Gerodiversity - How Facing Adversity across the Lifespan can Foster Workplace Resilience
Bibliographic record
Abstract
Symposium Topic: Older Women Who Work: Examinations of Grey and Grit Chair: Lisa Hollis-Sawyer, PhD, Northeastern Illinois University Participants: Mary Gergen, PhD, Penn State University; and Ellen Cole, PhD, The Sage Colleges. When Just Getting by Is Getting Old: Women Working in Later Life to Pay the Bills- Monica Teixeira, MA, Columbia College. The Impact of Aging and Authentic Leadership in a Higher Education Latina Leader - Julie Hicks Patrick, PhD, West Virginia University. Appalachian Grit and Older Working Women - Niva Piran, PhD, University of Toronto, Toronto, ON, Canada. Missions Continued: The Meaning of Work in Older Women’s Lifelong Journey - Ashley Stripling, PhD, and Jodie Maccarrone, MS, Nova Southeastern University. Gerodiversity - How Facing Adversity Across the Lifespan Can Facilitate Workplace Resilience Discussant: Ellen Cole, PhD, The Sage Colleges
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".