SOLID WASTE POLICIES AND CLIMATE CHANGE – THE CASES OF FEDERAL BRAZIL AND CANADA
Bibliographic record
Abstract
The objective of our paper is to scrutinize the ongoing dynamics surrounding waste and climate policies within the federal democracies of Brazil and Canada. Our research design is guided by four primary inquiries. Firstly, we look at the integration of the circular economy concept within waste and climate policies. Secondly, we explore the extent to which concerns regarding solid waste are assimilated into climate mitigation and adaptation strategies. Thirdly, we assess the degree to which social inclusion is upheld as foundational principle within climate and waste policies. Lastly, we investigate the governance practices that have been put in place to promote effective intergovernmental and state-society cooperations. Initially, we illuminate the commonalities and disparities between Brazilian and Canadian federalism, emphasizing the challenges of policy coordination—a critical prerequisite for a smooth integration and successful execution of solid waste and climate policy initiatives. In the core section of our paper, we adopt a comparative lens to analyze both policy domains, focusing on (a) institutional frameworks, competencies, and the characteristics of the policy-making processes in each country; (b) legislative measures, programs, plans, and policy instruments to elucidate the policy fields and the linkages between them. We conclude our paper by juxtaposing the experiences of both countries and suggesting potential ways for mutual learning to improve federal democratic responses in tackling these complex and interlinked challenges.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".