The concept of sepulchral rights in Canada and the USA in the age of genomics: hints from Iceland
Bibliographic record
Abstract
Analysis of law relating to the human body and its parts is accentuated by increasing genomic research utilizing human body tissues. (1) No doubt, the human genome project is the largest and most representative paradigm of such modern biomedical research endeavors. (2) Genomic research studies the function of individual genes in the human body and how they interact with one another and the environment. Advocates of genomics argue that it will help explain the genetic basis of diseases and shed light on therapeutic interventions. (3) To carry out genomic research, however, scientists need to obtain bodily materials, tissue samples and relevant health information from human sources. In some circumstances, samples may be obtained from deceased persons. Though genomics promises enormous health benefits, it raises significant social, ethical, and legal concerns. (4) For instance, genetic information obtained from a tissue sample may relate to intimately private matters such as race, height, susceptibility or predisposition to disease, behavioral traits, and sex. This type of information reaches beyond the sample source to family members and its illegitimate exploitation could have wide-ranging impacts entailing ostracism, discrimination in employment, and insurance. (5) Accordingly, the right to control current and future uses of a genetic material or information obtained from it has become pivotal.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.038 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".