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Record W4256680634 · doi:10.1037/e677112011-003

Emotional and instrumental aggressiveness and body weight loss

2007· dataset· en· W4256680634 on OpenAlexaff
Sébastien Paradis, J. Martin Ramirez, Michel Cabanac

Bibliographic record

VenuePsycEXTRA Dataset · 2007
Typedataset
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsWeight lossPsychologyInstrumental variableComputer scienceMedicineObesityInternal medicineMachine learning

Abstract

fetched live from OpenAlex

Violence and aggressiveness are social concerns.Also, at a time of rising prevalence of obesity, many people tend to control their body weight through dieting.We analyzed the impact of weight loss on aggressiveness: 150 participants completed anonymously two questionnaires assessing their aggressiveness, age, sex, diet, recent body weight change, reasons of recent body weight changes, and perceived difficulties related to those changes.Results showed that participants who had deliberately lost weight reported higher aggressiveness than controls, but passive weight-losers did not.The raised aggressiveness was stronger for hostile aggression than for instrumental aggression.Such a rise is likely to be due to the discomfort associated with opposing body weight set-point.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.014
Threshold uncertainty score1.000

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 integrity0.0010.001
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 head0.019
GPT teacher head0.339
Teacher spread0.320 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2007
Admission routes1
Has abstractyes

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