Becoming prosocial peers: The roles of temperamental shyness and mothers’ and fathers’ elaborative emotion language
Bibliographic record
Abstract
Abstract Using a sample of 99 2‐ to 5‐year‐olds (51 girls, 48 boys), we evaluated whether parent‐reported temperamental shyness was associated with prosocial behaviors with same‐aged peers, and considered parenting (use of elaborative emotion language) and parent and child gender as possible moderators of relations between shyness and prosocial behaviors. Active and passive forms of prosocial behavior were evaluated when children were with familiar and unfamiliar peers. There were no direct associations between shyness and peer prosocial behaviors. Fathers’ emotion elaboration predicted more active prosocial behavior with familiar peers. There were significant moderating effects of parental emotion language, and parent and child gender, on relations between shyness and prosocial behavior. When mothers used more emotion elaboration, less shy children showed more active prosocial behavior toward unfamiliar peers and less passive prosocial behavior with familiar peers. Conversely, when fathers used more emotion elaboration, more shy boys engaged in more active prosocial behaviors with unfamiliar peers. These findings suggest that multiple social and contextual factors influence whether shy children become proactive helpers and sharers. Shy boys may particularly benefit from emotion elaboration from fathers whereas less shy children may be most prepared to benefit from mothers’ emotion language.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".