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Record W6965119923 · doi:10.32920/29156093.v1

'Age is Different': Revisiting the Contemporary Understanding of Age Discrimination in the Employment Setting

2025· article· en· W6965119923 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicDiscrimination and Equality Law
Canadian institutionsnot available
Fundersnot available
KeywordsAge discriminationContext (archaeology)Supreme courtTest (biology)Employment discriminationRacismPrejudice (legal term)

Abstract

fetched live from OpenAlex

<p>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.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.170
GPT teacher head0.392
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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