A Behaviour‐Based Peer‐Nomination Measure of Social Withdrawal in Children
Bibliographic record
Abstract
Research on social withdrawal has been impeded by problems in the definition of the construct and in its measurement. The purpose of this study was to develop a behaviourally‐based peer‐nomination measure useful in measuring two types of withdrawal: Inhibited/Wary and Self‐Conscious/Anxious. In Study 1, we report the development of the measure. We examined whether this two‐factor structure would be supported by data obtained from multiple informants. Data were collected from children in grades 3, 5, and 7, and their peers and teachers. Intercorrelations of peer‐, teacher‐, and self‐reports of the Behaviour‐Based Peer‐Nomination Measures of social withdrawal supported the validity of the Inhibited/Wary dimension; teacher‐peer agreement also provided support for the validity of the Self‐Conscious/Anxious dimension. In Study2, the Behaviour‐Based Peer‐Nomination Measure of Social Withdrawal was cross‐validated against observed play behaviour with 120 boys and girls. Results revealed some concordances between observational and peer‐nomination data.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".