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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".