Call for Action: Provinces and Territories Must Protect our Genetic Information
Bibliographic record
Abstract
The Genetic Non-Discrimination Act (GNDA), passed by Parliament in 2017, seeks to protect Canadians’ genetic information. The GNDA establishes certain criminal prohibitions to the use of genetic information and also amends federal employment and human rights legislation to protect against genetic discrimination. However, we argue that the GNDA alone is insufficient to protect Canadians given constitutional limitations on the powers of the federal government. Areas of profound importance relating to genetic discrimination are governed by the provinces and territories. We identify three key areas of provincial/territorial jurisdiction relevant to protection against genetic discrimination and outline the applicable legislative environments. We identify problems with the status quo and set out the gaps and limitations of relying solely on the GNDA. We conclude that provinces and territories need to amend their human rights, employment, and insurance legislation to ensure comprehensive protection of Canadians’ genetic information.\nLa Loi sur la non-discrimination génétique (la Loi), adoptée par le Parlement en 2017, vise à protéger les informations génétiques des Canadiens. La Loi établit certaines règles pénales interdisant l’utilisation des informations génétiques et modifie également d’autres lois fédérales en matière d’emploi et de droits de la personne afin de protéger contre la discrimination génétique. Cependant, nous soutenons que la Loi seule est insuffisante pour protéger les Canadiens étant donné les limitations constitutionnelles des pouvoirs du gouvernement fédéral. Des domaines d’une grande importance relatifs à la discrimination génétique sont régis par les provinces et les territoires. Nous identifions trois domaines clés de compétence provinciale/territoriale pertinents pour la protection contre la discrimination génétique et décrivons les environnements législatifs applicables. Nous identifions les problèmes liés au statu quo et exposons les lacunes et les limites du recours à la seule Loi sur la non-discrimination génétique. Nous concluons que les provinces et les territoires doivent modifier leurs lois relatives aux droits de la personne, à l’emploi et aux assurances afin d’assurer une protection complète des renseignements génétiques des Canadiens.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".