Links Between Middle-Childhood Trajectories of Family Dysfunction and Indirect Aggression
Bibliographic record
Abstract
Using data from three waves of a large Canadian data set, this research examined the relationship between middle-childhood trajectories of family dysfunction and indirect aggression. The authors applied family systems, developmental psychopathology, and life-course conceptualizations to meet this objective. The data analytic strategy used separate multivariate logits to examine this relationship, with and without the extent to which other possible explanations (acting as control variables) predict belonging to the highest family dysfunction trajectory. These included marital transition, socioeconomic status, family size, and depressive symptoms experienced by the adult most knowledgeable about the child (mostly mothers). The authors also explored possible interactions between indirect aggression and these explanatory variables. Supporting their hypothesis for both boys and girls, prolonged-duration high doses of family dysfunction were associated with the most extreme developmental trajectories of indirect aggression during middle childhood. Results showed gender specificity with respect to the influence of the explanatory variables on family dysfunction. For girls, the link between family dysfunction and indirect aggression persisted above and beyond such contextual influences. For boys, the relationship became unimportant once contextual factors were taken into account.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".