From Grass Roots to Pharma Partnerships: Breast Cancer Advocacy in Canada
Bibliographic record
Abstract
From Grass Roots to Pharma Partnerships examines the development, over a twenty-year span, of alliances between grass roots breast cancer groups in Canada and the pharmaceutical industry. I conclude that these alliances alter the advocacy content and style of the groups in ways that silence grass roots critique and support the policy goals of the pharmaceutical industry. I present my results in three parts. First, narrative accounts depict differing responses among breast cancer organizations to overtures from the pharmaceutical industry, from outright rejection, to a middle stance that I label “pragmatic ambivalence,” to acceptance of complete funding by pharma. Second, I describe three features of Canada’s policy landscape that have been altered by the successive adoption of neoliberal polices and which affect the character of patients’ movements.These are: 1) the failure of Canada’s healthcare system to adapt to a generation of new, expensive drugs; 2) a weakening of the system of cost controls, drug approvals, and the regulation of truth claims about drugs; and 3) policies that restrict funding to, and critical advocacy by, the civil society sector. The third section of my results describes the gradual transition of the breast cancer movement over two decades, from small, local, independent groups to a national network of organizations, many of which now rely heavily on the pharmaceutical industry for support. A series of case studies of projects carried out by groups and funded by “big pharma” illustrates subtle misrepresentations of the state of knowledge about new cancer drugs. These findings suggest that patient-centred breast cancer groups need sources of funding and information independent of the pharmaceutical industry if they are to contribute a user’s perspective to pharmaceutical policy about drugs whose effects are still largely uncharted.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".