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Record W2996235193

Solid waste management practices in two northern Manitoba first nations communities: community perspectives on the issues and solutions

2016· dissertation· en· W2996235193 on OpenAlexfundaboutno aff
Ahmed Oyegunle

Bibliographic record

VenueMspace (University of Manitoba) · 2016
Typedissertation
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsSolid waste managementEnvironmental planningPolitical scienceGeographyEnvironmental ethicsSociologyMunicipal solid wasteWaste managementEngineering
DOInot available

Abstract

fetched live from OpenAlex

For many First Nations in Northern Manitoba, solid waste management remains a serious, albeit under-researched, problem. A case study of solid waste management was undertaken in Garden Hill and Wasagamack First Nations, two remote fly-in communities in northern Manitoba. Solid waste management practices were investigated through interviews, participatory documentary video and laboratory analysis. Findings indicated that poor funding, absence of any recycling programs and lack of waste collection services contributed to indiscriminate burning and disposal in public places. Laboratory analyses revealed that soil samples from the dump sites had arsenic, chromium, lead, zinc and copper above CCME guidelines. These elevated levels of toxic metals are of significant concern as the dumps are both nearby to water bodies, and have no restrictions, such as fence, to prevent public access. Appropriate funding for solid waste programs, including waste collection and disposal facilities, recycling and training programs are highly recommended to safeguard community health.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.540

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0190.005
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.273
Teacher spread0.240 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

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