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Record W4211232681 · doi:10.3390/su14031852

Public Perceptions of Legislative Action to Reduce Plastic Pollution: A Case Study of Atlantic Canada

2022· article· en· W4211232681 on OpenAlexafffundabout
Shen Molloy, Andrew S. Medeiros, Tony R. ‎Walker, Sarah Saunders

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsWorld Wildlife Fund CanadaDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLegislatureLegislationGovernment (linguistics)Legal actionBusinessPublic policyFocus groupEnvironmental planningPolitical sciencePublic economicsMarketingGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

Government-led legislation is a key strategy to reduce plastic pollution; however, societal perception can heavily influence government intervention for environmental issues. To understand the public acceptability of government action to reduce plastic pollution, we examine the perception of existing and upcoming legislative action on single-use plastics (SUPs) by means of a structured survey with additional semi-structured interviews. Our focus is on the four Atlantic provinces of Canada, which was the first region in Canada to implement provincial-wide legislation for plastic reduction at the consumer level in 2019. Results show strong public support (77%, n = 838) for bans of SUP bags at the consumer level, and for further plastic pollution reduction legislation. However, the level of support differed between regions and by demographics. Semi-structured interviews show that decision makers should increase efforts in raising consumer awareness and standardizing regulations across jurisdictions for smoother transitions prior to legislative action.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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 teacher head, not a consensus.

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

Citations42
Published2022
Admission routes3
Has abstractyes

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