Uncertain risks, responsibilities & regulations : the ethics & control of PGD in Canada
Bibliographic record
Abstract
The current state of preimplantation genetic diagnosis technology is presented, as are the biological principles and medical procedures that make it possible. The arguments of both proponents and those with social and ethical reservations about the broader implications of the technique are carefully reviewed, and the limitations of the dominant medical model approach to the technique are exposed. A discussion of reproductive autonomy in light of emerging testing applications of PGD not directly related to the avoidance of serious genetic abnormalities in the resulting child demonstrates the complexity of both clinical decision-making and public policy formulation with regard to PGD. Recently proposed legislation in Canada reflects such complexities, and highlights the lack of social consensus on the appropriate uses of, and restrictions on, PGD. A variety of "soft law" instruments, notably professional codes of practice and research guidelines implemented by institutional ethics committees, may mitigate some of the uncertainty surrounding PGD in Canada, but their limited applicability and espousal of the medical model approach render questionable their capacity to reconcile tolerance of pluralism with respect for human life, diversity, and reproductive autonomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".