Mean kids become mean adults: Trajectories of indirect aggression from age 10 to 22
Bibliographic record
Abstract
Although much is known about the development of physical aggression across the lifespan, far less is known about the developmental pattern of indirect aggression from childhood to adulthood. Accordingly, we examined the self-reported use of indirect aggression from age 10 to 22 in a randomly drawn sample of 704 Canadians. A person-centered approach was used to capture intraindividual change and heterogeneity in development. Four childhood (age 10-18) indirect aggression trajectories were identified: (1) a very low decreasing group (64.8%), (2) a low decreasing group (26.0%), (3) a low-to-moderate increasing group (5.1%), and (4) a moderate increasing group (4.1%). There were more girls than boys in the moderate increasing group (75.9% vs. 24.1%). Two adulthood (age 19-22) indirect aggression trajectory groups were also identified: (1) a low decreasing group (82.6%), and (2) a moderate stable group (17.4%). No sex differences were found among adults' use across the two trajectories. When we examined the prediction of indirect aggression use in adulthood from indirect aggression use in childhood, we found that children who followed a moderate increasing trajectory from age 10 to 18 were nine times more likely to follow a moderate stable trajectory from age 19 to 22, while children who followed a low-to-moderate increasing trajectory across childhood were 14 times more likely to follow a moderate stable trajectory across adulthood (compared to the very low decreasing group). Given the negative impact indirect aggression has on others, intervening early to derail this pattern of abuse is justified.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".