Introduction: biosocialities, genetics and the social sciences
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.029 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".