Bibliographic record
Abstract
This article uses the narrative of one woman, Clara Larson, to explore changes over time in the experiences of illness available to women diagnosed with breast cancer. To claim that different illness experiences become available at different times is simply to acknowledge that experiences of disease are shaped not only by the individual circumstances of disease sufferers and the particular character of their pathologies, but by culturally, spatially and historically specific regimes of practices. This article explores the impact of social movements on the regime of breast cancer and makes four contributions to the scholarship on illness experience. First, it offers the concept disease regime as a way of conceptualising the structural shaping of illness experience. Second, it demonstrates the value of incorporating social movements more thoroughly into the study of illness experience. Third, it proposes that social movements change illness experiences in two ways: (1) by changing the sufferer or her relationship to the regime's practices; and (2) by changing and expanding the regime's actual practices. And fourth, it demonstrates how gender and sexuality are constituted within disease regimes and are challenged by social movements. This article is informed by four years of ethnographic research conducted in the San Francisco Bay Area between 1994 and 1998, supplemented by historical research and more than 40 taped interviews and oral histories with current and former breast cancer patients, activists, educators, scientists, support group leaders and volunteers.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.080 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".