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Intergenerational transmission of theory‐of‐mind

2008· article· en· W2067969245 on OpenAlexafffund
Mark A. Sabbagh, Elizabeth Seamans

Bibliographic record

VenueDevelopmental Science · 2008
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyTheory of mindCorrelationDevelopmental psychologyTask (project management)Cognitive psychologyCognition

Abstract

fetched live from OpenAlex

We examined whether individual differences in children's performance on a scaled battery of theory-of-mind tasks was predicted by parents' performance on an adult theory-of-mind task. Forty-six 3-year-old children and their parents participated in this study when children were aged 2;11 to 4;0. Thirty dyads returned 6 months later for a second assessment. After statistically controlling for relevant covariates, we detected a positive correlation between parents' and children's theory-of-mind scores. The correlation was significant at both time points, and was robust when data were aggregated across the two sessions. These results provide some evidence for intergenerational transmission of theory-of-mind abilities in a typically developing sample, and possible mechanisms underlying this relation are discussed.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.032
GPT teacher head0.284
Teacher spread0.252 · 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

Citations33
Published2008
Admission routes2
Has abstractyes

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