MétaCan
Menu
Back to cohort
Record W3121975356

Fake It Till You Make it: Policymaking and Assisted Human Reproduction in Canada

2014· article· en· W3121975356 on OpenAlexaffabout
Françoise Βaylis, Jocelyn Downie, Dave Snow

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsParliamentReimbursementLegitimacyReproductionBusinessLawPublic administrationPolitical scienceLaw and economicsHealth careEconomicsPolitics
DOInot available

Abstract

fetched live from OpenAlex

The Assisted Human Reproduction Act (AHR Act) came into effect in 2004. The AHR Act stipulates in s.12 that no reimbursement of expenditures incurred in the course of donating gametes, maintaining or transporting in vitro embryos, or providing surrogacy services is permitted, except in accordance with the regulations and with receipts. Ten years later, Health Canada still has not drafted the regulations governing reimbursement. Section 12 is therefore still not in force. Health Canada and others have asserted that there is a Health Canada policy on reimbursement and that reimbursement with receipts is legally permissible. We dispute the existence of such a policy and its legitimacy (if it exists). We also challenge the decision by Health Canada not to produce regulations and thereby make it possible for Parliament to bring s.12 into force. This intentional lack of action is worrisome on at least two fronts. First, it sidesteps the processes required for regulations and thereby ducks the Parliamentary oversight very deliberately built into the AHR Act. Second, it leaves Canadians who provide and who access assisted human reproduction uncertain about what is and is not permitted, and therefore fearful of, or at risk of, prosecution. We conclude that Health Canada should take the steps necessary to put regulations in front of Parliament so that Parliament will then be able to pass regulations and bring s.12 into force. Canadians should demand no less.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.308
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicMulticultural Socio-Legal StudiesFrench-language works237,207