Strengthening environmental protection for a healthier Canada Act : the right to a healthy environment & accountability in chemicals management
Bibliographic record
Abstract
Chemical exposure is causing health effects unequally to peoples across Canada. CEPA has been revised to give Canadians the right to a healthy environment. In this thesis I investigate if these recent changes to CEPA effectively protect or prevent against harmful chemical exposure, and if not, what avenues are available to strengthen this right? To achieve this aim, I reviewed Federal Acts that deal with chemicals. Using a political economy lens, I performed a content analysis of CEPA to explore the distribution of power and accountability within the Act. Finally, I conducted 6 semi structured interviews with people directly involved in the changes made to CEPA, probing their ideas regarding these changes. The interviews centred around Sarnia Ontario, coined the ‘Chemical Valley’ for its petrochemical presence. Sarnia serves as a case study of disproportionate chemical exposure to Indigenous peoples in Canada. Three key findings came out of my content analysis: the narrow criteria for the type of information included in regulatory decision-making do not include exploratory natural science research and social science research, importantly excluding intersectional exposure context; economic growth is embedded into the ways in which the environment is protected through CEPA and; weak language and legislative loopholes allow for continued growth in the chemical industry at the expense of government and peoples in Canada. Three themes stood out in my interviews: each participant highlighted the role of language in legislation as either a current weakness or as a tool to strengthen the implementation of a right to a healthy environment; unclear distribution of responsibility towards the health of Indigenous peoples between provincial and federal governments has paved the way for the present situation in Chemical Valley and is preventing change and; the implementation of a right to a healthy environment is only as successful as citizens’ ability to access justice through the legislation. iii My study resulted in recommendations regarding removing ambiguousness & omission, recentering government obligations, and prioritizing new pathways for justice.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.033 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| 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".