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Record W2757535482 · doi:10.1037/dev0000417

The codevelopment of sympathy and overt aggression from middle childhood to early adolescence.

2017· article· en· W2757535482 on OpenAlexaff
Antonio Zuffianò, Tyler Colasante, Marlis Buchmann, Tina Malti

Bibliographic record

VenueDevelopmental Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Toronto
FundersJacobs FoundationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsPsychologyAggressionSympathyDevelopmental psychologyLate childhoodEarly childhoodPoison controlAdolescent developmentSocial psychology

Abstract

fetched live from OpenAlex

We assessed the extent to which feelings of sympathy and aggressive behaviors codeveloped from 6 to 12 years of age in a representative sample of Swiss children (N = 1,273). Caregivers and teachers reported children's sympathy and overt aggression in 3-year intervals. Second-order latent curve models indicated general mean-level declines in sympathy and overt aggression over time, although the decline in sympathy was relatively small. Importantly, both trajectories were characterized by significant interindividual variability. A bivariate second-order latent curve model revealed a small-moderate negative correlation between the latent slopes of sympathy and overt aggression, suggesting an inverse codevelopmental relationship between the constructs from middle childhood to early adolescence. In terms of predictive effects, an autoregressive cross-lagged model indicated a lack of bidirectional relations between sympathy and overt aggression, underscoring the primacy of the variables' rank-order stability. We discuss the codevelopment and developmental relations of sympathy and aggression, their potential conjoint social-emotional mechanisms, and the practical implications thereof. (PsycINFO Database Record

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.930

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.304
Teacher spread0.273 · 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

Citations37
Published2017
Admission routes1
Has abstractyes

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