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Record W4380767130 · doi:10.38126/jspg220202

Closing the Intention-Action Gap: Behaviorally-Aligned Strategies for Effective Plastic Pollution Reduction

2023· article· en· W4380767130 on OpenAlexaffabout
Bhuvanesh Awasthi, Anne‐Teresa Birthwright, María Inés Carabajal, Larisse Faroni‐Perez

Bibliographic record

VenueJournal of Science Policy & Governance · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsGovernment of Canada
Fundersnot available
KeywordsPlastic pollutionClosing (real estate)PollutionAction (physics)BusinessEnvironmental planningEnvironmental resource managementEnvironmental science

Abstract

fetched live from OpenAlex

This brief provides an overview of existing or proposed policies that aim to mitigate plastic pollution in four countries across the Americas (Argentina, Brazil, Canada, and Jamaica), as well as how a behavioral framework can be used to analyze and develop effective plastic pollution mitigation policies. At times, policy efforts do not always produce the desired outcomes, resulting in an intention-action gap, which is a disconnect between policy intentions and the everyday behavior of stakeholders. This brief highlights behaviorally-aligned recommendations to reduce the impact of plastic waste and pollution. The ultimate goal of this policy brief is to provide policymakers an insight into behaviorally-aligned policy perspectives to address plastic pollution in their respective countries.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.946
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.340
Teacher spread0.316 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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