Validation of the Intention Attribution Test for Children (IAC)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Intention Attribution Test for Children (IAC) was created to assess hostile attribution bias in preschool- and early school-aged children. It comprises 16 cartoon strips presenting situations in which one character (either a child or an adult) causes harm to another, either intentionally, accidentally (nonintentional), or without his or her intention being clear (ambiguous). Its validity was tested on 233 children aged 4 to 12 years. Exploratory factor analysis and item response theory models demonstrated support for a single factor of hostile attribution bias for the ambiguous and nonintentional items. Analyses revealed, however, that the intentional items did not contribute to this same overall construct of hostile intention attribution bias. Correlations with the Social Perception Test and with sociometry suggest good validity of the IAC. The IAC may be a useful instrument for research and in the context of therapeutic intervention addressing socially inappropriate behavior in childhood.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it