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Record W2028539476 · doi:10.1215/03616878-2009-026

Imagining the Consequences of Human Biotechnology

2009· article· en· W2028539476 on OpenAlexaff
Md. Mahmudur Rahman Bhuiyan

Bibliographic record

VenueJournal of Health Politics Policy and Law · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBioethicsField (mathematics)SociologyEngineering ethicsHuman lifeEthical issuesHuman healthAffect (linguistics)EpistemologyEnvironmental ethicsSocial sciencePolitical scienceLawPhilosophyHumanityMedicineEngineering

Abstract

fetched live from OpenAlex

Given that there are huge uncertainties about how contemporary advances in human biotechnology are going to affect human life, different scholars try to understand the situation in different ways. This essay reviews four recent books devoted to fostering a fundamental ethical, philosophical, and practical understanding of the issues related to the use of genetic technologies in the human medical field. Each of these books sees these issues from different perspectives and provides different understandings of the issues through a distinct process of analysis. While the first three books provide significant theoretical insights about the consequences of contemporary biotechnological advances, explaining them from different philosophical perspectives and contexts, the fourth book shows how bioethics itself is being instrumentalized in the United States in favor of scientific and medical practices.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.996
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.049
Scholarly communication0.0090.011
Open science0.0010.003
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.119
GPT teacher head0.440
Teacher spread0.321 · 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.

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

Citations0
Published2009
Admission routes1
Has abstractyes

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