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Record W4297239978 · doi:10.1002/pits.22793

Psychometric properties of the School Kindness Scale in Hong Kong, mainland China, and the Philippines

2022· article· en· W4297239978 on OpenAlexaboutno aff
Jet U. Buenconsejo, Olivia I. P. Pianpiano, Jesus Alfonso D. Datu

Bibliographic record

VenuePsychology in the Schools · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsKindnessMainland ChinaPsychologyScale (ratio)MainlandMeasurement invarianceSocial psychologyChinaHostilityStructural equation modelingConstruct (python library)PsychometricsDevelopmental psychologyGeographyConfirmatory factor analysisTheologyStatistics

Abstract

fetched live from OpenAlex

Abstract Studies have shown that the School Kindness Scale (SKS) has adequate psychometric properties in different societies such as Canada, Turkey, and the Philippines. However, there is scarce evidence on the psychometric validity of this scale across multiple societies and educational contexts. This study explores the cross‐national invariance of the SKS among high school students in the Philippines, Hong Kong, and mainland China. Results showed that the modified unidimensional model of school kindness with correlated error terms on item number 2 and 3 had the most optimal fit. There was evidence supporting partial invariance of the modified unidimensional model of school kindness across setting and year level, and full invariance across gender. Whereas school kindness also demonstrated positive correlations with perceived academic performance in Hong Kong and mainland China, this construct was linked to higher emotional and social engagement in math in all contexts.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.080
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.308
Teacher spread0.283 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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