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Record W4313400063 · doi:10.1016/j.clwas.2022.100074

A review on solid waste management in Canadian First Nations communities: Policy, practices, and challenges

2022· review· en· W4313400063 on OpenAlexafffundabout
Ziyu Wang, Zhikun Chen, Chunjiang An

Bibliographic record

VenueCleaner Waste Systems · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsConcordia University
FundersConcordia University
KeywordsSolid waste managementEnvironmental planningSustainable developmentState (computer science)Political scienceProcess (computing)Diversity (politics)BusinessEconomic growthMunicipal solid wasteEnvironmental resource managementPublic administrationEngineeringGeographyLawEconomics

Abstract

fetched live from OpenAlex

There is an increasing concern regarding the sustainable solid waste management (SWM) around the world. This review first summarizes First Nations’ social behavior, culture, environmental perspectives, and sustainable development perspectives. The review then introduces the laws and regulations regarding the First Nations SWM system and environment at the federal, provincial, and municipal levels. These laws and regulations can be described as the basic guides, restrictions, and useful improvement tools for First Nations SWM. After this, several technical reports and journal articles focusing on Canada’s Native First Nations are used to provide a comparison between urban and remote communities and allow analyses of three key issues (open dumping, open burning, and COVID-19) so as to describe the current state of Canadian First Nations SWM practices and demonstrate their diversity in Canada. Lastly, the potential First Nations SWM improvement strategies are introduced through education and training, process improvement, and zero-waste possibilities.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.729
Threshold uncertainty score0.539

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.014
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.123
GPT teacher head0.344
Teacher spread0.221 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations27
Published2022
Admission routes3
Has abstractyes

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