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Record W2059716931 · doi:10.1037/a0025357

Children's moral evaluations of reporting the transgressions of peers: Age differences in evaluations of tattling.

2011· article· en· W2059716931 on OpenAlexafffund
Ivy Chiu Loke, Gail D. Heyman, Julia Forgie, Anjanie McCarthy, Kang Lee

Bibliographic record

VenueDevelopmental Psychology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsFanshawe CollegeUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentSocial Sciences and Humanities Research Council of CanadaNational Institutes of Health
KeywordsMarine transgressionPsychologyDevelopmental psychologyCognitionSocial psychologyCognitive developmentObserver (physics)Psychiatry

Abstract

fetched live from OpenAlex

The way children evaluate the reporting of peers' transgressions to authority figures was investigated. Participants, ages 6-11 years (N = 60), were presented with a series of vignettes, each of which depicted a child who committed either a minor transgression (such as not finishing the vegetables at lunch) or a more serious transgression (such as stealing from a classmate). Participants were asked to evaluate the decision of a child observer who either did or did not report the transgression to a teacher. Younger children considered reporting to be appropriate for both types of transgressions, but older children considered reporting to be appropriate for major transgressions only. Results are interpreted with reference to (a) a changing peer culture in which the social cost of reporting transgressions increases and (b) a developmental change in children's cognitive capabilities.

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)0.004
metaresearch head score (Gemma)0.037
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 score0.008
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
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 head0.278
GPT teacher head0.463
Teacher spread0.184 · 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

Citations42
Published2011
Admission routes2
Has abstractyes

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