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Record W2530198299 · doi:10.1017/cbo9781316424155

Biologically Modified Justice

2016· book· en· W2530198299 on OpenAlexaff
Colin Farrelly

Bibliographic record

VenueCambridge University Press eBooks · 2016
Typebook
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsQueen's University
Fundersnot available
KeywordsDistributive justiceEconomic JusticeLotteryLaw and economicsIntervention (counseling)PovertyPolitical scienceEnvironmental ethicsSociologyLawEconomicsMedicinePhilosophy

Abstract

fetched live from OpenAlex

Theories of distributive justice tend to focus on the issue of what constitutes a fair division of 'external' goods and opportunities; things like wealth and income, opportunities for education and basic liberties and rights. However, rapid advances in the biomedical sciences have ushered in a new era, one where the 'genetic lottery of life' can be directly influenced by humans in ways that would have been considered science fiction only a few decades ago. How should theories of justice be modified to take seriously the prospect of new biotechnologies, especially given the health challenges posed by global aging? Colin Farrelly addresses a host of topics, ranging from gene therapy and preimplantation genetic diagnosis, to an 'anti-aging' intervention and the creation and evolution of patriarchy. This book aims to foster the interdisciplinary dialogue needed to ensure we think rationally and cogently about science and science policy in the twenty-first century.

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)0.003
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.026
Scholarly communication0.0070.007
Open science0.0010.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0160.003

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 head0.098
GPT teacher head0.273
Teacher spread0.175 · 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
GenreOther

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

Citations15
Published2016
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooksSame topicNeuroethics, Human Enhancement, Biomedical InnovationsFrench-language works237,207