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Record W2902752104

Moral Evils v. Health and Safety Evils: The Case of an Ovum ‘Obtained’ From a ‘Donor’ and Used By the ‘Donor’ in Her Own Surrogate Pregnancy

2018· article· en· W2902752104 on OpenAlexvenueno aff
Pamela White

Bibliographic record

VenueCanadian journal of family law · 2018
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsEgg donationPregnancyGynecologyAndrologyMedicineObstetricsBiologyGenetics
DOInot available

Abstract

fetched live from OpenAlex

This paper critically examines the amendment made in 2012 to section 10(2)(c) of the Assisted Human Reproduction Act, 2004 mandating the screening and testing of “obtained” ovum “donated” by a “donor” and used in her own surrogate pregnancy. The amendment at section 10(1) of the Act cites the federal government’s obligation to reduce harm to human health and safety arising from use of sperm or ova for human reproduction, including the risk of disease transmission. This paper argues that the amendment mandating the screening and testing of surrogate ova when used by the surrogate in her own surrogate pregnancy creates a dangerous liminal regulatory space; one that transforms the surrogate into a third-party donor yet she incurs no health and safety risk to herself as she is the recipient of her own ova embryo. Genetic implications for the surrogate-born child makes a stronger case in support of mandatory testing, however the amendment imposes no similar screening and testing regime on the usual category of traditional surrogates: women who bear genetically-related children conceived through artificial insemination (IUI) rather than IVF. The paper questions the application of a health and safety evil that the amendment seeks to address. It suggests the real evil is a moral one whereby criminal code sanctions are being employed to discourage traditional surrogacy when practiced as a result of assisted reproduction techniques.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.312
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.306
Teacher spread0.259 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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