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Record W2983163652 · doi:10.1037/pspi0000223

Food restriction and the experience of social isolation.

2019· article· en· W2983163652 on OpenAlexaff
Kaitlin Woolley, Ayelet Fishbach, Ronghan Wang

Bibliographic record

VenueJournal of Personality and Social Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsBooth University College
FundersUniversity of ChicagoCornell University
KeywordsPsychologyIsolation (microbiology)Social isolationSocial psychologySocial approvalPsychotherapist

Abstract

fetched live from OpenAlex

Across 7 studies, food restrictions increased loneliness by limiting the ability to bond with others through similar food consumption. We first found that food restrictions predict loneliness using observer- and self-reports among children and adults (Studies 1-3). Next, we found mediation by the experience of worry and moderation by eating similar food as others. When restricted individuals were unable to bond over a meal (i.e., they ate different vs. the same food as others), they worried. These "food worries" mediated the effect of restrictions on loneliness (Studies 4 and 5). Moving to controlled experiments, manipulating the presence of a food restriction for unrestricted individuals increased reported loneliness (Study 6). This effect replicated in an experiment that capitalized on a naturally occurring food restriction-the holiday of Passover-where Jewish observers were restricted from eating chametz (leavened food; Study 7). Overall, while both food restrictions and loneliness are on the rise; this research found they may be related epidemics. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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)0.006
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.036
GPT teacher head0.360
Teacher spread0.324 · 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

Citations44
Published2019
Admission routes1
Has abstractyes

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