Bibliographic record
Abstract
In October 2021, the Quebec government introduced Bill 2, which proposed significant reforms to Quebec’s surrogacy laws. Quebec’s Minister of Justice emphasized that Bill 2 was intended to better account for the needs and lived realities of Quebec families. He also specified that its surrogacy provisions aimed to prioritize the best interests of children while also protecting surrogates’ rights. This article explores Bill 2’s proposed reforms to Quebec’s surrogacy laws and their implications for surrogates, intended parents, and the children born through these arrangements. I argue that while Bill 2’s proposals would go a long way towards legitimizing and regulating surrogacy arrangements, the bill leaves a series of important questions unanswered and may have effects that run counter to lawmakers’ objectives. The reader should be advised that when this article was being edited and typeset in preparation for publication, the Quebec government re-introduced Bill 2 as « Bill 12 » with some minor modifications to its provisions pertaining to surrogacy. Notably, Bill 12 clarifies that the surrogate will remain the child’s legal parent, and a court will not have discretion to modify the child’s filiation, in the event the surrogate refuses to give up their parental rights following the birth. However, given the similarities between the two bills, this article’s commentary and criticisms remain highly relevant and timely with respect to Bill 12’s proposed reforms to Quebec’s surrogacy laws.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".