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Record W2011892910 · doi:10.7202/051189ar

Obesity as a Covered Disability Under Employment Discrimination Law: An Analysis of Canadian Approaches

2005· article· en· W2011892910 on OpenAlexvenueaboutno aff
Harris L. Zwerling

Bibliographic record

VenueRelations industrielles · 2005
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsnot available
Fundersnot available
KeywordsJurisdictionStatuteLawPolitical scienceHuman rightsOverweightEmployment discriminationObesityMedicine

Abstract

fetched live from OpenAlex

Since the passage of the first anti-discrimination laws in North America, the number of groups or classes protected has slowly expanded. People with disabilities are one of the more recent groups to be covered by such laws. No Canadian human rights statute includes the obese or overweight as a separate designated group. British Columbia is the only jurisdiction in which obesity per se has been found to be a covered disability. All other Canadian jurisdictions that have explicitly addressed the issue require claimants to prove that their obesity is a disabling condition and has an underlying involuntary medical cause. This paper examines the treatment of the obese under the antidiscrimination laws of the Canadian federal and provincial jurisdictions, focusing primarily upon the laws of Ontario. Its central thesis is that despite the reticence of various human rights agencies, there is ample legal basis for including obesity as a covered disability under human rights law.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.222
Threshold uncertainty score0.902

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0110.015
Science and technology studies0.0250.011
Scholarly communication0.0070.002
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.202
GPT teacher head0.417
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2005
Admission routes2
Has abstractyes

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