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Record W4240414826 · doi:10.32920/ryerson.14655387

The effects of dietary restraint and portion-control packaging on snack consumption behaviour

2021· preprint· en· W4240414826 on OpenAlexaff
Katey Ellen Park

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of GuelphSystems, Applications & Products in Data Processing (Canada)
Fundersnot available
KeywordsDietingConsumption (sociology)Portion sizePsychologyControl (management)Social psychologyFood scienceMedicineObesityComputer scienceChemistryEndocrinologyAestheticsArtWeight lossArtificial intelligence

Abstract

fetched live from OpenAlex

The present study investigated the effects of package size on consumption behaviour when either body image or dietary concerns are activated, in restrained and unrestrained eaters. Portion-control packaging has recently emerged under the assumption that carefully-controlled portion sizes help limit consumption of palatable snacks. While there is reasonably good support for this in most populations, recent findings suggest that portion-control packaging may paradoxically increase consumption for restrained eaters (Coelho Do Vale, Pieters, & Zeelenberg, 2008; Scott et al., 2008). Consistent with prior research, we hypothesized that restrained eaters activated for dieting or body image concerns are more likely to deem larger packaged-sized treats as “unacceptable” and decrease intake. Similarly, activated restrained eaters are more likely to deem smaller packaged-sized treats as “acceptable” and thus paradoxically increase intake. However, the present study did not find support for hypotheses. Theoretical reasons as to why results were not what researchers anticipated are proposed.

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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.317
Teacher spread0.296 · 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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