It’s Time to Retire Ageism against Older Workers
Bibliographic record
Abstract
Ageism in the workplace can have significant implications for older adults. While every individual should feel equal and have the right to employment free from discrimination due to age, many practices and policies do not appear to uphold this right in the labour market. Institutional practices and policies seem to perpetuate stereotypes about older people. A “pro-aging” campaign to raise awareness about ageism in the workplace was run in the City of Toronto in 2019. The campaign included posters and pop-up advertising of a fake aging cream and research on attitudes toward aging and understanding the “too old” narrative as part of inclusive workplace policies. Workplace diversity policies often do not include age considerations, and understanding the factors that lead to ageism may allow for the development of strategies to help combat it. Age-diverse workplaces may gain competitive advantage by learning to harness the power of intergenerational relationships.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".