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Record W4401747957 · doi:10.1016/j.appet.2024.107642

Perceiving less but wasting more: The relationship between perceived resource scarcity and consumer food waste

2024· article· en· W4401747957 on OpenAlexafffund
Bonnie Simpson, Rhiannon MacDonnell Mesler, Katherine White

Bibliographic record

VenueAppetite · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsUniversity of British ColumbiaUniversity of LethbridgeWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsScarcityResource scarcityFood wasteResource (disambiguation)WastingProduct (mathematics)BusinessNatural resource economicsPsychologyEnvironmental economicsEconomicsMicroeconomicsWaste managementEngineeringMedicineComputer science

Abstract

fetched live from OpenAlex

Consumers' food-related behaviors often culminate in significant food waste. Surprisingly however, limited attention has been given to psychological reasons why this occurs. Across four studies, this research suggests that, because perceived resource scarcity activates a resource acquisition goal, under conditions where product scarcity is not present it leads consumers to engage in inaccurate over-acquisition of resources (i.e., food), resulting in greater waste. Studies 1a (quasi-experimental field study) and 1b (lab experiment) test the role of perceived resource scarcity in predicting food acquisition and waste. Studies 2a and 2b are correlational and measure household food waste to demonstrate that resource acquisition accuracy mediates the relationship between perceived resource scarcity and food waste.

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)0.002
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.050
GPT teacher head0.254
Teacher spread0.204 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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