'Age is Different': Revisiting the Contemporary Understanding of Age Discrimination in the Employment Setting
Bibliographic record
Abstract
The author argues that the current test for age discrimination in Canada, which is based on the Supreme Court of Canada’s decision in R. v. Kapp and which requires that discrimination be motivated by or perpetuate stereotyping or prejudice, has led adjudicators to fail to come to grips with wrongful ageism in the workplace. The fact that everyone ages, and that distinctions based on age may in the past have benefitted the same people who are now harmed by those distinctions, has in the author’s view been given too much weight, thereby making discrimination against senior workers too easy to justify. She proposes that the legal test for age discrimination should focus on wrongs done in the present, and should not take account of any past or future benefits which may be attributed to a distinction drawn on the basis of age. On the basis of what the author calls the Dignified Lives Approach, she argues that an age-based distinction should be held to be discriminatory if it violates any of these five principles: people of all ages must be assessed on their merits, must be treated as equals, must have enough means to live lives of dignity, must be socially included, and must retain their autonomy. Using as examples four recent cases of alleged age-based discrimination in the employment context decided by Canadian courts and administrative tribunals, the author demonstrates how the Dignified Lives Approach would in her view be more sensitive to different types of age discrimination and would bring more just outcomes.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.097 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".