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Record W2166302003 · doi:10.1375/aedp.26.2.138

Theory of Mind Performance in Middle Childhood: Australian Normative and Validation Data

2009· article· en· W2166302003 on OpenAlexaff
Larry Cashion

Bibliographic record

VenueThe Australian Educational and Developmental Psychologist · 2009
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsChild, Adolescent and Family Mental Health
FundersCharles Sturt UniversityUniverzita Karlova v Praze
KeywordsPsychologyNormativeTheory of mindConfirmatory factor analysisValidityDevelopmental psychologyReliability (semiconductor)PsychometricsCognitionStructural equation modelingPsychiatry

Abstract

fetched live from OpenAlex

Abstract Theory of mind tests are regularly used in childhood research and clinical practice for autism spectrum disorders in Australia. Despite this, there is little empirical evidence that the tests used in the United Kingdom and the United States have validity for Australian children. Furthermore, many tests believed to assess ‘advanced’ or ‘higher-order’ theory of mind have not had their reliability or validity rigorously evaluated. In the current study, seven theory of mind tasks were administered to 216 Australian children aged 6 to 12 years as part of a larger research project. While Australian children performed similarly to previous research samples on some tasks, there were marked discrepancies on others. Despite these differences, the validity of using a three-factor structure of first-, second-, and higher-order theory of mind tasks was supported by subsequent confirmatory factor analysis. Methodological issues accounted for some differences between the Australian and previously reported data. However, there were also some cross-cultural aspects of the results that require further investigation.

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)0.007
metaresearch head score (Gemma)0.015
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 score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.144
GPT teacher head0.367
Teacher spread0.223 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

Explore more

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