Bibliographic record
Abstract
In this paper, we consider the implementation of the employment (age) regulations in the United Kingdom in both an historical and an international context, drawing on evidence from the United States, Australia, Canada, New Zealand and Ireland. The workplace is not the only setting in which age-based discrimination makes itself felt, but research does indicate that employment provides the most common ground of age discrimination complaints and that age discriminatory practice affects older age groups more than younger age groups. Survey evidence does indicate that many employers regard older workers as equal to if not preferable to younger workers in a number of key employment and productivity areas, while other evidence suggests that age does have an influence on the recruitment decisionmaking process. Age is a factor in the recruitment process and few employers have mechanisms to encourage older workers to apply for jobs, even though many have a formal written policy on equal opportunities (also in respect of age). Older workers are also less likely to be offered job-related training and education. Thus, chronological age does figure in the workplace and in employers’ policies and practices in relation to recruitment and promotion, access to training, retirement, and redundancy. These policies and practices influence the way in which older employees plan for withdrawal from the labour force. The implementation of the employment equality (age) regulations as of October 2006 indeed changes practice and policy in the workplace, but does not signal the end of retirement, and international evidence would indicate that only a small proportion of agerelated cases are taken to tribunal, with an even smaller proportion being upheld there.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".