Breast cancer gene research and medical practices transnational perspectives in the time of BRCA
Bibliographic record
Abstract
The discovery of the two inherited susceptibility genes BRCA1 and BRCA2 in the mid-1990s created the possibility of predictive genetic testing and led to the establishment of specific medical programmes for those at high risk of developing breast cancer in the UK, US and Europe. In the intervening fifteen years, the medical institutionalisation of these knowledge-practices and accompanying medical techniques for assessing and managing risk have advanced at a rapid pace across multiple national and transnational arenas, whilst also themselves constituting a highly mobile and shifting terrain. This unique edited collection brings together cross-disciplinary social science research to present a broad global comparative understanding of the implications of BRCA gene research and medical practices. With a focus on time-economies that unfold locally, nationally and transnationally (including in Brazil, Canada, France, Germany, India, Italy, the UK and the USA), the essays in this volume facilitate a re-reading of concepts such as prevention, kinship and heredity, and together offer a unique, timely and comparative perspective on these developments. The book provides a coherent structure for examining the diversity of practices and discourses that surround developments linked to BRCA genetics, and to the evolving field of genetics more broadly. It will be of interest to students and scholars of anthropology, sociology, history of science, STS, public health and bioethics. © 2014 selection and editorial material Sahra Gibbon, Galen Joseph, Jessica Mozersky, Andrea zur Nieden and Sonja Palfner; individual chapters: the contributors.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".