Understanding the Use of "Genetic Predisposition" in Canadian Legal Decisions
Bibliographic record
Abstract
Since the advent of the Human Genome Project in 1989, the ethical, legal, and social implications inherent in future genetic science and its applications have worried researchers and scholars in law and ethics. Concern that the results of genetic testing might be used to discriminate against particular individuals and groups of individuals has been paramount, prompting calls for specific legislation to protect against genetic discrimination. Against this backdrop we sought to investigate instances of genetic discrimination in Canadian legal decisions. We searched Canadian court and administrative tribunal decisions, using the key words “genetic predisposition” and its cognates, and found none that took up the issue of genetic discrimination. However, in 468 decisions, “genetic predisposition” was used by courts and tribunals when describing the causal origins of health related conditions. Genetic predisposition was cited with respect to numerous health conditions, and in various areas of law, in particular criminal, family, workers’ compensation, and tort. In several criminal law decisions, genetic predisposition served to explain the origin of a mental health condition in addressing the issue of criminal responsibility. The predominant use in family law was in describing a child’s health condition in crown wardship and youth protection proceedings. In workers’ compensation and tort, genetic predisposition was used to argue whether the claimant’s condition was inherited rather than related to the workplace or the negligence of the defendant. Genetic predisposition, when used to argue the issue of disease causation on a balance of probabilities, reflects “geneticization”: the tendency to describe the underlying basis of health and disease as genetic. Geneticization, like genetic discrimination, can be problematic. Specifically, both may exaggerate the extent to which genetic information is exceptional and determinative of health and disease outcomes. Also, geneticization, like genetic discrimination, may marginalize people on a perceived genetic basis.
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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 | 0.029 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.043 | 0.027 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".