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Record W2052957992 · doi:10.1037/0278-6133.21.3.299

Can fruits and vegetables and activities substitute for snack foods?

2002· article· en· W2052957992 on OpenAlexaff
Gary S. Goldfield, Leonard H. Epstein

Bibliographic record

VenueHealth Psychology · 2002
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsChildren's Hospital of Eastern Ontario
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsSnack foodFood choiceFeeding behaviorFood scienceSedentary behaviorEnvironmental healthPsychologyMedicinePhysical activityPhysical therapyBiology

Abstract

fetched live from OpenAlex

This study investigated the choice of snack foods versus fruits and vegetables and enjoyable sedentary behaviors using a computerized behavioral choice task. Thirty-nine participants were provided the choice of earning points for snack foods or fruits and vegetables (Condition 1) or snack foods or enjoyable sedentary behaviors (Condition 2). The behavioral cost to gain access to snacks increased across trials, whereas the behavioral costs to obtain alternatives to snack foods remained constant across trials. Results showed that when costs for snack foods and alternatives were equal, participants chose snack foods, but as the behavioral costs increased, participants shifted choice to the alternatives. The switch point for both alternatives was equal. Results suggest that fruits and vegetables and sedentary activities can substitute for snack foods when the behavioral cost for snack foods is increased.

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)0.004
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.190
GPT teacher head0.469
Teacher spread0.278 · 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

Citations93
Published2002
Admission routes1
Has abstractyes

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