Bibliographic record
Abstract
Using the results of a study to investigate women’s accounts of risks for breast cancer as a case study, this article examines the phenomenological, social and political implications of prevailing discourses on risk. The study is situated within the critical social science literature that argues that ‘risk’ has become one of the defining cultural characteristics of western society. As in other areas of life, the notion of risk has become central to discourses related to individual health; that is, ‘risk’ has become a common construct around which health in western society is described, organized and practised, both personally and professionally. This article argues that women’s health experiences, and the discourses on risk that shape those experiences, are produced within the same ideological context within which particular diagnostic/screening technologies are developed and deployed. Using governmentality as a framework, it is argued that current discourses on risk both make possible and are made possible by particular diagnostic/screening technologies. These discourses on risk also both reflect and reproduce notions of the ‘entrepreneurial subject’ and are, thus, consistent with a prevailing neo-liberal political rationality. The article concludes with a discussion of some of the questions for further research which are raised by this analysis.
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 | 0.028 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.090 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".