The Prism of Age: Managing Age Diversity in the Twenty-First-Century Workplace
Bibliographic record
Abstract
Abstract Increases in older adults’ labour force participation rates have resulted in a workforce that is ‘more grey’ than it was at the turn of the millennium (see Chapter 2 for workforce ageing statistics). Between 1997 and 2007, the labour force participation rates of adults who were aged 55–64 years increased from 49.6 per cent to 57.1 per cent in Canada, from 41.1 per cent to 51.3 per cent in Germany, and from 54.1 per cent to 61.8 per cent in the United States (OECD 2009a). This extended labour force attachment among older adults reflects a set of new economic realities, emergent priorities of today’s 50+ age group and altered expectations for the productive roles that different societies around the world are setting for older adults, including continued participation in paid employment. (Morrow-Howell et al. 2009)
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".