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Record W4229784844 · doi:10.32920/ryerson.14651859.v1

Development of a sustainable and inclusive solid waste management system in Colombia

2021· preprint· en· W4229784844 on OpenAlexaff
Jessica Edmonds

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of Toronto
FundersAgence Nationale de la Recherche
KeywordsContext (archaeology)BusinessMunicipal solid wasteInformal sectorSolid waste managementOrder (exchange)Developing countrySustainabilityInclusion (mineral)NormativeService (business)Economic growthWaste managementEconomicsEngineeringMarketingFinancePolitical scienceGeography

Abstract

fetched live from OpenAlex

Managing solid waste is a pressing environmental issue worldwide. This is especially observed in developing countries, where the main concern is to provide the service of waste collection, usually lacking a formal recycling program. Instead, recycling is often conducted by an informal sector composed of recyclers-by-trade. What has been found is that the current informal recycling sector - if approached differently - can offer a financially viable and an environmentally and culturally sound solution. A case study approach was chosen and questionnaires were conducted with recyclers-by-trade and dealers in Cali, Colombia. An interview was conducted with the President of the Recyclers' Association of Bogotá, Colombia. A normative system is proposed as an alternative context-based solution in developing countries that focuses on the inclusion of organized recyclers-by-trade into the formal solid waste management in order to increase recycling rates, extend the lifespan of landfills and improve the living and working conditions of this informal recycling sector.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.000
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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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