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Record W2169323616 · doi:10.1111/bioe.12067

Assisted Reproduction and Distributive Justice

2013· article· en· W2169323616 on OpenAlexaffabout
Vida Panitch

Bibliographic record

VenueBioethics · 2013
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsReproductionAppealDistributive justiceEconomic JusticeIdeal (ethics)Law and economicsPoliticsCollective responsibilitySociologyBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

The Canadian province of Quebec recently amended its Health Insurance Act to cover the costs of In Vitro Fertilization (IVF). The province of Ontario recently de-insured IVF. Both provinces cited cost-effectiveness as their grounds, but the question as to whether a public health insurance system ought to cover IVF raises the deeper question of how we should understand reproduction at the social level, and whether its costs should be a matter of individual or collective responsibility. In this article I examine three strategies for justifying collective provisions in a liberal society and assess whether public reproductive assistance can be defended on any of these accounts. I begin by considering, and rejecting, rights-based and needs-based approaches. I go on to argue that instead we ought to address assisted reproduction from the perspective of the contractarian insurance-based model for public health coverage, according to which we select items for inclusion based on their unpredictability in nature and cost. I argue that infertility qualifies as an unpredictable incident against which rational agents would choose to insure under ideal conditions and that assisted reproduction is thereby a matter of collective responsibility, but only in cases of medical necessity or inability to pay. The policy I endorse by appeal to this approach is a means-tested system of coverage resembling neither Ontario nor Quebec's, and I conclude that it constitutes a promising alternative worthy of serious consideration by bioethicists, political philosophers, and policy-makers alike.

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)0.012
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.343
Threshold uncertainty score0.681

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0110.067
Scholarly communication0.0080.004
Open science0.0020.004
Research integrity0.0080.004
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 head0.113
GPT teacher head0.379
Teacher spread0.266 · 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 designTheoretical or conceptual
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
Published2013
Admission routes2
Has abstractyes

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