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Record W4291116582 · doi:10.20849/jed.v6i4.1229

Developmental Trajectories of Preschool Children’s Bullying Behavior: Prediction of Peer Relationships

2022· article· en· W4291116582 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Education and Development · 2022
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Toronto
FundersMinistry of Education of the People's Republic of China
KeywordsNormativeLatent growth modelingPsychologyDevelopmental psychologyPerspective (graphical)Intervention (counseling)Peer groupPeer acceptance

Abstract

fetched live from OpenAlex

A longitudinal study was conducted with 425 preschool children during a one-and-a-half-year period to investigate the developmental trajectory of preschool children’s bullying behavior and the prediction of peer relationships in this trajectory. The latent growth curve model (LGCM) and mixed growth model (GMM) were conducted on Mplus to investigate the normative development trajectory and heterogeneity of preschool children’s bullying behavior. Results showed that: (1) In general, preschool children’s bullying increased with age, and two significantly different sub-trajectories were identified through the model-fitting parameters. One was the ―low-slow increasing‖ group, accounting for 88.47% of participants; the other group was the ―high-fast decreasing‖ group, accounting for 11.53% of participants. (2) Peer rejection positively predicted preschool children’s bullying behavior, while peer acceptance and gender were not significant predictors. This study uncovered preschool children’s bullying behavior from a developmental perspective and provided further theoretical evidence for future intervention programs to reduce bullying behaviors.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.296
Teacher spread0.260 · 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