Preventing Breast Cancer: An Analysis of Canada's Regulatory Regime for Chemicals
Bibliographic record
Abstract
Breast cancer is the most commonly diagnosed cancer in women worldwide. The incidence rates are such that one in nine Canadian women will be diagnosed in her lifetime. While social science research has demonstrated the influence of social, political, economic, and environmental factors on health outcomes, many still emphasize the role of traditional risk factors for breast cancer, such as family history or diet. However, these factors are unable to account for the increased incidence of the disease in industrialized countries. This leads to a call for more attention to the environmental links to breast cancer, including the ‘everyday exposures’ to toxic substances that we experience in our daily lives, which often include mammary carcinogens and endocrine disrupting chemicals. The Canadian Environmental Protection Act, 1999 and the federal government’s Chemicals Management Plan are designed to protect the environment and the entire Canadian population from risks associated with exposure to toxic substances. This dissertation research examines the body of Canadian law, policy and practice which encompasses Canada’s regulatory regime for toxic substances. The regime is evaluated from a population health and primary prevention perspective. I asked: are the laws, policies and practices governing the everyday exposures to toxic substances in Canada inherently precautionary? And do they enact a primary prevention approach to women’s health? \n \nThe primary prevention of environmental health outcomes has not been a strong feature of public health policy and legislation in Canada, despite the efforts of environmental breast cancer activists who advocate for a precautionary approach. This research is steeped in politicized debates as it engages with issues central to women’s health, risk and the environment. I examine how the issues are communicated and understood, who the policies are designed to protect, and where the burden of risk is presumed to lie. I consider whether the policies capture the need for prevention and action related to women’s health. This research seeks to identify gaps in the law, policy and practice and in doing so, concludes that women’s health is not adequately protected from detrimental health outcomes as a result of everyday exposures to toxic substances, including breast cancer.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".