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Record W3049042451 · doi:10.1016/j.appet.2020.104841

Propelling pride to promote healthy food choices among entity and incremental theorists

2020· article· en· W3049042451 on OpenAlexaff
Julia Storch, Jing Wan, Koert van Ittersum

Bibliographic record

VenueAppetite · 2020
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsUniversity of Guelph
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsPrideMalleabilityPsychologyRecallSocial psychologyPsychological interventionIntervention (counseling)Cognitive psychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Past research suggests that people's beliefs about the malleability of their body weight influence their motivation to engage in healthful behaviors: people who perceive their body weight as fixed (entity theorists) engage less in healthful behaviors than people who perceive their body weight as changeable (incremental theorists). Accordingly, current health interventions frequently aim at shifting entity theorists' beliefs about the malleability of their body weight. Instead of trying to change these beliefs, we test whether the elicitation of pride from past achievements can serve as an intervention to promote healthful behaviors among entity theorists. In addition, we contrast the effect of pride recall among entity theorists with the effect among incremental theorists. Specifically, we find that entity theorists chose healthier behaviors upon the recall of pride related and unrelated to the health domain - the source of pride does not seem to matter. For incremental theorists, however, the source of pride does matter. While health-related pride led them to persist in making healthy food choices, health-unrelated pride instilled reward-seeking behavior among incremental theorists. Prompting health-related pride might be a viable motivational tool to promote healthy food choices, as it is beneficial for entity theorists without thwarting the motivation of incremental theorists.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.129
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

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

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 head0.040
GPT teacher head0.313
Teacher spread0.273 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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