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Record W2961758891 · doi:10.3389/fmed.2019.00157

Canada's Assisted Human Reproduction Act: Pragmatic Reforms in Support of Research

2019· article· en· W2961758891 on OpenAlexafffundabout
Tania Bubela, Erika Kleiderman, Zubin Master, Ubaka Ogbogu, Vardit Ravitsky, Amy Zarzeczny, Bartha Maria Knoppers

Bibliographic record

VenueFrontiers in Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversity of ReginaMcGill UniversityMcGill Genome CentreUniversité de MontréalUniversity of AlbertaSimon Fraser University
FundersHealth CanadaGenome AlbertaGovernment of AlbertaOttawa Hospital Research InstituteCanadian Institutes of Health ResearchCanadian Medical AssociationGenome CanadaAlberta InnovatesMcGill UniversityStem Cell NetworkUniversity of Ottawa
KeywordsReproductionHuman reproductionPolitical sciencePsychologyBiologyEcologyAnatomy

Abstract

fetched live from OpenAlex

Canada’s Assisted Human Reproduction Act is long overdue for Parliamentary review. We argue that the current regulation of research using human reproductive materials is not proportionate, not responsive to the uncertain threats posed to human and environmental health and safety, and is not considerate of diverse values in a democratic society. We propose tailored regulatory carve-outs for in vitro research for currently prohibited activities, such as gene editing, and for the exercise of Ministerial Discretion for access by Canadians to experimental in vivo interventions that are currently prohibited, such as mitochondrial replacement therapy. Our recommendations are bounded by constitutional constraints that recognize political and practical challenges in keeping oversight of this research under Federal jurisdiction, whether conducted in academic or private sectors. The proposed nuanced regulatory scheme should be overseen by a new national Agency, modeled on a blend of the Canadian Stem Cell Oversight Committee and Assisted Human Reproduction Canada.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.042
GPT teacher head0.374
Teacher spread0.332 · 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

Citations10
Published2019
Admission routes3
Has abstractyes

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