Bibliographic record
Abstract
Commentators argue that statutory prohibitions with the force of the criminal law should not be used to regulate new reproductive technologies (NRTs) and novel genetic technologies (NGTs). Bill C-13, the Assisted Human Reproduction Act, however, codifies 10 criminal bans. This paper considers the merits of the various arguments levied against Bill C-13, and the corollary claim that only a "non-prohibitive" model of legislation befits NRTs and NGTs. Three types of arguments are used to critique criminal bans: (1) "Structural" arguments hinge on the constraints of the Canadian legal system - legislation complete with prohibitions runs afoul of the Constitution Act 1867, violates the Canadian Charter of Rights and Freedoms, and cannot keep pace with scientific progress. (2) "Consequentialist" arguments focus on the potential results of enacting a statute carrying criminal bans - criminalization will chill research, drive research underground, encourage researcher forum shopping, fuel public misperception by reinforcing genetic determinism, and effectively foreclose important dialogue on NRTs and NGTs. (3) "Theoretical" arguments relate to the very nature of criminal law - prohibitions will be unenforceable; the criminal law, a model of "command and control", will be ineffective in shaping research practice; and moral ambiguity can support only regulation, as consensus is a sine qua none for criminal bans. All the arguments in opposition to criminal bans prove unpersuasive; moreover, they fail to substantiate a non-prohibitive alternative for NRT-NGT regulation. Bill C-13 should therefore be proclaimed into law; perhaps then commentators will actually theorize about the harms, or lack thereof, of particular NRTs and NGTs.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".