Trajectories and predictors of indirect aggression: results from a nationally representative longitudinal study of Canadian children aged 2–10
Bibliographic record
Abstract
The purposes of this study were to model the development of indirect aggression among a nationally representative sample of 1,401 Canadian children aged 4 at T2, 6 at T3, 8 at T4 and 10 at T5, and to examine predictors of trajectory group membership from T1 (age 2) child, familial, and parenting variables. Using a semi-parametric group-based modeling approach, two distinct trajectories were identified: "increasing users" comprising of 35% of the sample and "stable low users" comprising of 65% of the sample. Using logistic regression analyses to distinguish these two groups, we found that for girls, more frequent, increasing use of indirect aggression was associated with prior prosocial and physically aggressive behavior, low SES and low parental social support at age 2. For boys, increasing use of indirect aggression was associated with prior parenting issues at age 2-inconsistency and less positive parent-child interactions. Although this study provides unique information regarding the early development of indirect aggression and its predictors, more longitudinal research is needed to fully understand its development.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".