A dyadic perspective on evolutionarily relevant aggressive functions: Links to victim characteristics
Bibliographic record
Abstract
Previous research identifies evolutionarily-relevant motives for the use of aggression in adolescence, including: competitive, impression management, reactive, and sadistic functions. We extend prior work by adopting a dyadic perspective and examining features of the perpetrator-target relationship and the characteristics of the target themselves. We used a sample of 278 Canadian adolescents (13–18 years old; 57 % boys; 54 % White) who engaged in aggression and a dyadic sample with their specific aggressive targets. We measured dyadic aggression (the types of aggression present in the dyad), dyadic relationship characteristics (reciprocity of aggression, friendship), target social characteristics (popularity, likability, social network position), and dyadic gender composition. Competitive aggression was related to direct aggression perpetrated by someone with lower or equal power (i.e., not bullying), reciprocal aggression, and male perpetrators. Impression management aggression was related to bullying, non-friend dyads, and targets with lower likability (though more overall friendships). Reactive aggression was related to direct aggression by someone with lower/equal power, and sadistic aggression was related to dyad friendship.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".