The Potential for Misusing “Genetic Predisposition” in Canadian Courts and Tribunals
Bibliographic record
Abstract
The fulfilment of promises made 25 years ago to link clinical conditions with gene sequences has allowed patients and families to better understand hereditary conditions and make choices regarding prevention, early detection and treatment. There have also been warnings issued over this period regarding other purposes for which genetic information may be used, such as discrimination against people with a genetic predisposition for the purposes of employment or insurance. There has also been concern that the “geneticization” of health might divert focus to individual, rather than social, determinants of health and away from the communal responsibility for health. These factors have not been comprehensively surveyed, particularly in law, in any jurisdiction. We analyzed the way in which genetic predisposition was used in Canadian courts and tribunals, including the clinical conditions for which genetic predisposition was cited, the area of law in which the case occurred, the legal issues that were raised, the results of the proceedings and the purposes for which genetic predisposition was introduced.
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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 | 0.031 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.038 | 0.011 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".