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Record W2613009104 · doi:10.1080/13642987.2017.1319704

Liability for harms caused <i>in utero</i>: new technologies, new problems

2017· article· en· W2613009104 on OpenAlexaff
Jason P. Blahuta

Bibliographic record

VenueThe International Journal of Human Rights · 2017
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsLakehead University
Fundersnot available
KeywordsCompensation (psychology)HarmDamagesTortLiabilityAbortionReproductive technologyPrejudice (legal term)Economic JusticeLawPolitical scienceLaw and economicsCriminologyPregnancyPsychologySociologyBiologySocial psychology

Abstract

fetched live from OpenAlex

The rationale for current tortious actions against mothers for injuries caused in utero is examined and defended as a means of the harmed born alive child seeking financial compensation from insurance companies. The upshot of this argument is that within this range of cases maternal tort immunity should be denied as it prevents the mother from being able to seek compensation for her injured born alive child. In effect, under current circumstances, maternal immunity hurts both the mother and her offspring. However, looking ahead to the likely progress of medical science and social justice studies, maternal immunity should be given serious consideration for cases that may emerge from a new and varied range of medical technologies. These technological developments include epigenetics, genetic engineering, and ectogenesis (the use of an artificial womb), and the issues these technologies raise – which include the status of the foetus and the permissibility of abortion, as well as the definition of ‘harm’ and ‘disability’ – are complex and have disturbing implications for maternal liability.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.297

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.083
GPT teacher head0.410
Teacher spread0.327 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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