MétaCan
Menu
Back to cohort
Record W4405829585 · doi:10.31025/2611-4135/2024.19433

SOLID WASTE POLICIES AND CLIMATE CHANGE – THE CASES OF FEDERAL BRAZIL AND CANADA

2024· article· en· W4405829585 on OpenAlexaffabout
Klaus Frey, Jutta Gutberlet, Gina Rizpah Besen

Bibliographic record

VenueDetritus · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsClimate changeMunicipal solid wasteEnvironmental planningEnvironmental scienceBusinessWaste managementEngineeringGeologyOceanography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.940

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0240.007
Scholarly communication0.0050.001
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.020
GPT teacher head0.271
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueDetritusSame topicMunicipal Solid Waste ManagementFrench-language works237,207