Reciprocating risks of peer problems and aggression for children’s internalizing problems.
Bibliographic record
Abstract
Three complementary models of how peer relationship problems (exclusion and victimization) and aggressive behaviors relate to prospective levels of internalizing problems are examined. The additive risks model proposes that peer problems and aggression cumulatively increase risks for internalizing problems. The reciprocal risks model hypothesizes that peer problems and aggression transact over time and mediate the effects of each other on prospective internalizing problems. Last, the internalizing risks model proposes that, in addition to aggressive behaviors, prior internalizing problems also provoke peer problems that, in turn, further elevate risks for prospective internalizing problems. Data came from a sample of 453 low-income, ethnically diverse children in kindergarten to Grade 3 who were assessed 3 times over 1 school term (in January, March and June). Findings supported the internalizing risks model. Four key pathways were found to increase risks for internalizing problems by the end of the school year; 2 of these routes were rooted in aggressive behaviors, and 3 paths operated indirectly via levels of peer problems in the spring. Children who were initially aggressive became excluded by peers by the spring, whereas children who initially showed more symptoms of depression and anxiety became victimized by peers by the spring. In turn, both peer exclusion and victimization increased prospective levels of internalizing problems by the end of the school year.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".