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Record W7108322544 · doi:10.24433/co.6019618.v1

Prevalence of plastic waste as a household fuel in low-income communities of the Global South (Stata Codes)

2025· other· en· W7108322544 on OpenAlexaff

Bibliographic record

VenueCode Ocean · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPlastic wastePlastic bagGlobal SouthHousehold wasteMunicipal solid wasteDeveloping countryUrban wasteWaste disposal

Abstract

fetched live from OpenAlex

Anecdotal evidence suggests that households burn plastics to manage waste and help satisfy their energy demand. To examine the prevalence, extent and reasons for use of plastic waste as household fuel, we conducted a survey with 1,018 key informants from cities in 26 countries in the Global South. Informants are purposively selected due to their familiarity with the living conditions in their communities. One-third of respondents reported being aware of plastic waste burning, with some reporting that their households engaged in this practice. Analyses of the data reveal significant correlations of plastic waste burning with both supply factors, such as , massive amount of plastic waste, and expensive clean fuels, and demand factors, including self-management of waste and energy needs. Expanding essential public waste management services and implementing programs that enhance the affordability of clean energy technologies, especially among marginalised and low-income communities, could reduce this health- and environment-damaging practice.

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)0.000
metaresearch head score (Gemma)0.002
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: Software · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.020
GPT teacher head0.251
Teacher spread0.231 · 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
GenreSoftware

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
Published2025
Admission routes1
Has abstractyes

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