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Record W2790212184 · doi:10.12927/hcpol.2018.25401

Research on Human Embryos and Reproductive Materials: Revisiting Canadian Law and Policy

2018· article· en· W2790212184 on OpenAlexafffundvenueabout
Ubaka Ogbogu, Amy Zarzeczny, Jay M. Baltz, Patrick Bedford, Jenny Du, Insoo Hyun, Yasmeen Jaafar, Andrea Jurisicova, Erika Kleiderman, Yonida Koukio, Bartha Maria Knoppers, Arthur Leader, Zubin Master, Minh‐Thu Nguyen, Forough Noohi, Vardit Ravitsky, Maeghan Toews

Bibliographic record

VenueHealthcare policy · 2018
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsMcGill Genome CentreOttawa HospitalTD Bank GroupUniversity of OttawaUniversity of ReginaLunenfeld-Tanenbaum Research InstituteUniversité de MontréalUniversity of Alberta
FundersMcGill University
KeywordsRelevance (law)Corporate governanceReproductive healthHuman researchReproductive technologyField (mathematics)BioethicsPolitical scienceEngineering ethicsEmbryoSociologyLawBiologyEngineeringManagementGeneticsDemographyEconomics

Abstract

fetched live from OpenAlex

Research involving human embryos and reproductive materials, including certain forms of stem cell and genetic research, is a fast-moving area of science with demonstrated clinical relevance. Canada's current governance framework for this field of research urgently requires review and reconsideration in view of emerging applications. Based on a workshop involving ethics, legal, policy, scientific and clinical experts, we present a series of recommendations with the goal of informing and supporting health policy and decision-making regarding the governance of the field. With a pragmatic and principled governance approach, Canada can continue its global leadership in this field, as well as advance the long-term health and well-being of Canadians.

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 imitation

Not 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.

metaresearch head score (Codex)0.093
metaresearch head score (Gemma)0.133
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.717
Threshold uncertainty score0.832

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0930.133
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.007
Science and technology studies0.0280.039
Scholarly communication0.0260.013
Open science0.0100.010
Research integrity0.0330.021
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.184
GPT teacher head0.518
Teacher spread0.335 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations7
Published2018
Admission routes4
Has abstractyes

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