Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper seeks to explore how organizations might create flexible work programs to attract and retain older workers. Design/methodology/approach Drawing on the literature on aging and work, the paper identifies an incoming HR challenge (leveraging an aging workforce), focuses on a strategy (designing flexible work programs) and reviews some innovative programs in Europe and North America. Findings The paper identifies three lessons. The first is to adopt a portfolio approach, which means to combine and integrate diverse dimensions of work flexibility (work schedule, number of hours worked and so on). The second is to offer flexible work options to retired employees. The third is to align flexible work opportunities with pension scheme options. Originality/value Labor market experts predict a steady increase in the number of older workers who will extend their work life or work during retirement. A number of surveys, in turn, report that people are more likely to seek flexible work options as they age. The paper provides practical advice that will help organizations to prepare for the demographic changes coming and to develop effective flexible work programs for older employees.
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it