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Record W4210343488 · doi:10.24124/2021/59188

Reducing plastic packaging at the Prince George Farmers' Market

2021· dissertation· en· W4210343488 on OpenAlexaboutno aff
Waranuch Tanubamrungsuk

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsVendorPlastic pollutionBusinessPlastic bagPlastic packagingMarketingConsumption (sociology)George (robot)Environmental economicsPollutionEngineeringWaste managementComputer scienceEconomics

Abstract

fetched live from OpenAlex

Plastic pollution, a global problem, contributes to detrimental impacts on the environment. To mitigate plastic pollution locally,farmers’ market scan play a role in reducing plastic consumption. Many studies have suggested that customers and vendors are aware of environmental problems. Yet vendor environmental awareness does not always result in appropriate actions, as many vendors still rely on plastic packaging. To investigate the reasons for such reliance, I conducted vendor interviews and a customer survey at the Prince George (B.C.) Farmers’ Market. The results show that the barriers to reducing plastic include packaging availability, characteristics, functions, and price. Based on interview results, I developed a manual to support vendors interested in reducing plastic packaging at their stalls. I also offer recommendations as to how policy makers can motivate vendors and customers to reduce plastic consumption, such as offering customers reusable bags and organizing plastic-free events.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.003

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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