Developmental deviations in happy victimization across early childhood predict behavioral adjustment in middle childhood
Bibliographic record
Abstract
Abstract This study examined whether the development of happy victimizing (HV) from early to middle childhood predicted prosocial and aggressive behaviors 3 years later. Participants included 150 children (50% female, Mage at study onset = 4.53 years) and their parents at four annual time points. At each time point, semi‐structured interviews were conducted to assess children's emotional expectations after committing hypothetical transgressions. Child and parent reports of children's prosocial behaviors and aggression were provided at the beginning and end of the study. Children who experienced faster declines in HV reported higher prosocial behaviors 3 years later, controlling for initial levels of prosocial behaviors. Children who exhibited increases or lesser declines in HV reported higher aggression at the study end. The development of HV was not related significantly to parent reports of prosocial behaviors and aggression in middle childhood. Findings confirm theorizing on the normative developmental trajectory of HV and suggest that deviations in HV across early childhood may partially explain later behavioral adjustment.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".