Post-Mortem Organs and Tissue Through a Property Law Lens: How Principles of Property Law Can Guide Lawmakers to a Better Organ Donation Framework
Bibliographic record
Abstract
Across Canada, transplant waitlists far outweigh the organs and tissue made available by the current post-mortem donation system. Every transplant donor is critical to alleviate the ever-growing demand for organs and tissue and there is significant potential for increased donations. Every donation statute in Canada invokes an exception to the deceased’s prior consent being binding. The next of kin’s power to veto decisions concerning post-mortem donations violates donor autonomy and neither the common law nor statutes explain how this veto should be interpreted and applied. The result is a system of organ donation that depends significantly on the altruism of surviving family members and ignores the need for increased donations. Issues with the current donation frameworks are illuminated by a wills and intestacy analogy. Basic principles of property law can and should guide lawmakers to meaningful reform of the donation systems. Post-mortem donative instructions should be viewed as sacrosanct, much like the testator’s instructions are viewed in the law of wills. Our choices concerning where our post-mortem body parts go are not safeguarded by the same protections afforded to our choices concerning property. This thesis explores the evolution of the common law of ownership regarding the human body and body parts, as well as the historical development of Canada’s donation legislation and the meaning of property in theories of jurisprudence. The enforceability of ownership rights in organs and tissue is consistent with popular definitions of property and substantiated further by ostensibly contrasting theoretical views of jurisprudence. This thesis contrasts presumed consent and mandated choice systems of organ donation and proposes an improved system of presumed consent that carefully qualifies the role of family, safeguards individual autonomy, and balances those components with the public need for increased donations.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.131 |
| Scholarly communication | 0.026 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".