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Record W2260269780

Introduction: biosocialities, genetics and the social sciences

2007· book-chapter· en· W2260269780 on OpenAlexaboutno aff
Sahra Gibbon, Carlos Novas

Bibliographic record

VenueUCL Discovery (University College London) · 2007
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipBiomedicineIdentity (music)BioethicsState (computer science)SociologyPolitical scienceSocial scienceBiologyGeneticsLaw
DOInot available

Abstract

fetched live from OpenAlex

Book description: Biosocialities, Genetics and the Social Sciences explores the social, cultural and economic transformations that result from innovations in genomic knowledge and technology. This pioneering collection uses Paul Rabinow’s concept of biosociality to chart the shifts in social relations and ideas about nature, biology and identity brought about by developments in biomedicine. Based on new empirical research, it contains chapters on genomic research into embryonic stem cell therapy, breast cancer, autism, Parkinson’s and IVF treatment, as well as on the expectations and education surrounding genomic research. It covers four main themes: * novel modes of identity and identification, such as genetic citizenship * the role of institutions, ranging from disease advocacy organizations and voluntary organizations to the state * the production of biological knowledge, novel life-forms, and technologies * the generation of wealth and commercial interests in biology. Including an afterword by Paul Rabinow and case studies on the UK, US, Canada, Germany, India and Israel, this book is key reading for students and researchers of the new genetics and the social sciences – particularly medical sociologists, medical anthropologists and those involved with science and technology studies.
\nDate:\t09 August 2007

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.795
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.221
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations23
Published2007
Admission routes1
Has abstractyes

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