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Record W2117114928 · doi:10.1136/jme.2008.025346

The case for re-thinking incest laws

2008· article· en· W2117114928 on OpenAlexaff
Colin Farrelly

Bibliographic record

VenueJournal of Medical Ethics · 2008
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsHarmSexual intercourseIntervention (counseling)CriminologyGermanPsychologySisterLawSociologyPsychiatryPolitical scienceSocial psychologyDemographyPopulationHistory

Abstract

fetched live from OpenAlex

The recent case of German siblings Patrick Stübing (age 30 years) and his sister Susan Karolewski (age 22 years) has reignited debate over the criminalisation of sexual intercourse among consanguine descendants. The primary justification for criminalising incest is the purported increased risk of genetic disabilities among offspring, but is criminalising sexual intercourse an empirically sound and proportionate response to this increased risk? To answer this question we must consider the specifics of the harm in question (eg, is it a harm to the child or a societal harm) and the magnitude of the harms of the intervention. The example of incest law has important implications for liberal societies. If we can justify imprisoning consenting adults for choosing partners who will increase the risk of having children with disabilities, then we set a troubling precedent for all couples who may pass on genetic disorders to their children.

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)0.024
metaresearch head score (Gemma)0.055
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 score0.033
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.055
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0090.044
Scholarly communication0.0100.017
Open science0.0030.007
Research integrity0.0330.044
Insufficient payload (model declined to judge)0.0070.002

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 head0.195
GPT teacher head0.456
Teacher spread0.261 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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