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Record W2786244228 · doi:10.1177/1103308817743371

Positivity Ratio Links Self-control Skills to Physical Aggression and Happiness in Young Palestinians Living in Gaza

2018· article· en· W2786244228 on OpenAlexaff
Michael Rosenbaum, Tammie Ronen, Izzeldin Abuelaish, Hod Orkibi, Liat Hamama

Bibliographic record

VenueYoung · 2018
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Toronto
FundersTel Aviv University
KeywordsHostilityAggressionAngerHappinessPsychologyDevelopmental psychologyClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

The study examined a potential underlying mechanism through which self-control skills (SCSs) may predict more happiness on the one hand and less hostility, anger and peer aggression on the other hand in an understudied sample of 744 Palestinian youngsters (Grades 8–12) from the Gaza Strip, a military conflict area. The hypothesized model was confirmed: self-reported SCS linked with happiness through positivity ratio as a mediator; SCS linked with physical aggression through the association of positivity ratio with hostility, and anger; and anger mediated the link between hostility and physical aggression. Additional analyses showed that girls scored higher than boys in SCS and boys scored higher than girls on positivity ratio, happiness, hostility, anger, and physical aggression. The study highlights the importance of imparting SCS to increase positivity ratio, so that, despite exposure to extreme adversity, youngsters in Gaza and elsewhere may experience not only less aggression but also more happiness.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.281
Teacher spread0.274 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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