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Record W3135365729 · doi:10.32920/22227502.v1

Unnecessary and Redundant? Evaluating Canada’s Genetic Non-Discrimination Act, 2017

2023· article· en· W3135365729 on OpenAlexaboutno aff
Kathleen Hammond

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsGenetic discriminationUltra viresLegislationJurisdictionParliamentPolitical scienceLawGenetic testingPoliticsBiologyGeneticsDoctrine

Abstract

fetched live from OpenAlex

<p> In 2017, Canada’s Genetic Non-Discrimination Act (GNDA) came into force. The GNDA’s enactment was prompted by concerns about genetic discrimination given the growing amount of genetic data being collected and stored by medical practitioners, but also by for-profit genetic testing companies. Critics have questioned whether discrimination of this kind even exists, and have suggested that the Act, and the changes that it required to the Canada Labour Code (CLC) and Canada Human Rights Act (CHRA), are redundant. In this paper, I explore the merits of these critiques by evaluating studies, anecdotal evidence, and case law on genetic discrimination. I argue that there is a small but growing body of evidence that genetic discrimination is occurring in Canada. The Act’s amendments to the CLC and CHRA may be somewhat redundant. However, given the growing trend of people thinking of diseases and conditions based on their genetic properties, the privacy concerns raised by genetic data, and the deterrent and symbolic potentials of the Act, I argue that it is an important new tool for preventing and prohibiting what could become a growing basis for discrimination. If the Act, which has been challenged for being ultra vires the Parliament of Canada’s jurisdiction over criminal law, is invalidated, its enactment, and this assessment of it, nonetheless provide insight into what desirable intra vires legislation might look like at the federal and provincial levels to deal with genetic discrimination.En 2017, la Loi sur la non-discrimination génétique (LNDG) est entrée en vigueur au Canada. Sa promulgation découle d’inquiétudes au sujet de la discrimination génétique eu égard à la somme croissante de données génétiques recueillies et conservées par les praticiens de la santé, mais aussi par les sociétés de dépistage génétiques du secteur privé. Les détracteurs se sont demandé s’il existe même des discriminations de ce genre et ont suggéré que la LNDG et les modifications qu’elle a imposées au Code canadien du travail (CCT) et à la Loi canadienne sur les droits de la personne (LCDP) sont inutiles. Dans cet article, l’auteure se penche sur les mérites de ces critiques en évaluant les études, la preuve empirique et la jurisprudence en matière de discrimination génétique. Elle soutient qu’il existe un ensemble limité, mais croissant, de preuves d’instances de discrimination génétique au Canada. Les modifications du CCT et de la LCDP suscitées par la Loi pourraient être inutiles à certains égards. Cependant, étant donné la tendance croissante à considérer les maladies et troubles en fonction de leurs propriétés génétiques, les préoccupations quant à la protection des renseignements personnels suscitées par la collecte des données génétiques, et les potentiels dissuasif et symbolique de la Loi, elle soutient qu’il s’agit là d’un nouvel outil important pour prévenir et interdire ce qui pourrait devenir un motif croissant de discrimination. Si la Loi, qui a été contestée au motif qu’elle excède la compétence du Parlement du Canada en matière de droit pénal, est invalidée, sa promulgation et l’évaluation qu’en fait l’auteure dans cet article fournissent néanmoins une idée de ce que pourrait être une loi souhaitable, promulguée dans les limites de la compétence fédérale et provinciale, pour traiter de la discrimination génétique. </p> <p> </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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.054
GPT teacher head0.357
Teacher spread0.303 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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