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Record W3122306211 · doi:10.1016/j.envc.2021.100029

Single-use plastic bag policies in the Southern African development community

2021· article· en· W3122306211 on OpenAlexaff
Joana Bezerra, Tony R. ‎Walker, C. Andrea Clayton, Issahaku Adam

Bibliographic record

VenueEnvironmental Challenges · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsDalhousie University
FundersAssociation of Commonwealth Universities
KeywordsBusinessPlastic bagEnforcementPlastic pollutionTourismPsychological interventionEnvironmental planningPublic policyEconomic growthPolitical sciencePollutionGeographyEconomicsEngineering

Abstract

fetched live from OpenAlex

The growing visibility of plastic pollution, particularly negative environmental impacts of single-use plastic bags, has entered the political debate, triggering policy interventions to control its manufacturing and use. This trend was also felt in Southern Africa, a region with high urbanization, leading to increased resource use and plastic consumption, heavily reliant on tourism, an industry highly impacted by plastic pollution. This paper reviews existing single-use plastic bag reduction policies in the Southern African Development Community (SADC). All 16 SADC members have announced a plastic bag reduction policy, but interventions vary in stages of implementation. Waste management emerged as the most important policy driver and over 55% of SADC members adopted a top-down approach in developing these policies to address these environmental challenges. Most SADC members with existing policies did not conduct public awareness campaigns, raising effectiveness issues. Further research on effective plastic bag reduction policy development, enforcement and monitoring would address an important knowledge gap.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
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 head0.044
GPT teacher head0.204
Teacher spread0.160 · 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 designObservational
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

Citations105
Published2021
Admission routes1
Has abstractyes

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