Bibliographic record
Abstract
Abstract Age is a critical issue for labour market policy. Both younger and older workers experience significant challenges at work. Despite the introduction of age discrimination laws, ageism remains prevalent. This book offers a roadmap for the future development of age discrimination law in common law countries, to better address workplace ageism. Drawing on theoretical, doctrinal, and empirical legal scholarship, and comparative perspectives from the United Kingdom, Australia, and Canada, the book provides a grounded critique of existing age discrimination laws and their enforcement, and puts forward concrete suggestions for legal reform and change. It examines the challenges and limitations of existing legal frameworks and the individual enforcement model for addressing age discrimination in employment, mapping the stages of claiming, negotiation, or alternative dispute resolution, and hearing and judgment, using mixed method case studies of the enforcement of age discrimination law in the United Kingdom and Australia. The book puts forward a fourfold model of reform to strengthen age discrimination law, to improve the individual enforcement model, strengthen positive equality duties, bolster the roles of statutory equality agencies, and enhance collective enforcement. The book critically considers how these options might address the limits of existing laws, and the practical measures necessary to ensure their success.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".