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Record W2051301078 · doi:10.1080/00221325.2010.533719

The Development of Prosocial Behaviors in Young Children: A Prospective Population-Based Cohort Study

2011· article· en· W2051301078 on OpenAlexafffundabout
Raymond H. Baillargeon, Alexandre Morisset, Kate Keenan, Claude L. Normand, Suganthiny Jeyaganth, Michel Boivin, Richard E. Tremblay

Bibliographic record

VenueThe Journal of Genetic Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcGill UniversityUniversité de MontréalUniversité LavalUniversité du Québec en OutaouaisUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsProsocial behaviorPsychologyDevelopmental psychologyPopulationNormativePersonal distressChild developmentDistressDemographyClinical psychology

Abstract

fetched live from OpenAlex

Researchers know relatively little about the normative development of children's behaviors aimed at alleviating distress or discomfort in others. In this article, the authors aim to describe the continuity and discontinuity in the degree to which young children in the general population are reported to exhibit specific prosocial behaviors. Data came from the Québec Longitudinal Study of Child Development. Consistent with Hay's model of prosocial development, the results show that there were about as many children who stopped exhibiting prosocial behaviors between 29 and 41 months of age as there were children who started doing so during this period. Further, gender differences (girls > boys) in prosocial behaviors are either emerging or at least increasing in magnitude, with girls being more likely to start and boys being more likely to stop exhibiting these behaviors between 29 and 41 months of age. Consistent with the early-onset hypothesis, children who exhibit prosocial behaviors at 17 months of age are less likely to stop exhibiting the same behaviors between 29 and 41 months of age. Otherwise, if they did not exhibit prosocial behaviors at 29 months of age, they are also more likely to start doing so in the following year.

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)0.001
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 score0.017
Threshold uncertainty score0.361

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)0.0000.000

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 head0.022
GPT teacher head0.308
Teacher spread0.287 · 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

Citations84
Published2011
Admission routes3
Has abstractyes

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