Bibliographic record
Abstract
This book has a personal side, as a coming-to-terms with an unfinished chapter of my past.When I was diagnosed with breast cancer in 1988, I already had spent two decades as a politically engaged journalist and editor, working mainly for feminist and consumer protection magazines.Following my diagnosis, I turned my research and writing to breast cancer and the politics of the disease, which at the time was scarcely explored.AIDS activists were blazing the way to a new form of disease activism, and I was struck by the contrasting invisibility and silence of breast cancer patients.That those of us living with a disease should contribute to shaping relevant health policies seemed obvious from a social justice perspective; that our lived experience was a necessary complement to the knowledge researchers and health practitioners had of the disease seemed equally clear.I cofounded Canada's first breast cancer patient advocacy group and wrote a book, Patient No More: The Politics of Breast Cancer, published in 1994 by Gynergy Books.As I wrote in the introduction to that book, the central task I saw for patients as activists was "to develop and advance a coherent perspective of our own.Our voice must be a counterweight to the medical point of view that dominates discussions of the disease" (p.xiii).Throughout the 1990s, I divided my time between writing about breast cancer and activism in the breast cancer movement, working not only in Canada but also in the United States, Australia, and Europe, including the United Kingdom.During this decade of intense engagement, the breast cancer movement grew and changed.I welcomed the growth, but one
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.477 | 0.297 |
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".