Toward an Integrative Approach to the Study of Positive-Affect-Related Aggression
Bibliographic record
Abstract
Research on aggression usually aims at gaining a better understanding of its more negative aspects, such as the role and effects of aversive social interactions, hostile cognitions, or negative affect. However, there are conditions under which an act of aggression can elicit a positive affective response, even among the most nonviolent of individuals. One might experience the "sweetness of revenge" on reacting aggressively to a betrayal or social rejection. A soldier may feel elated after "shooting to kill" in the name of the flag. There are many factors that contribute to the appeal of aggression, but despite growing interest in researching these phenomena, there is still no unitary framework that organizes existing theories and empirical findings and can be applied to a model to generate testable hypotheses. This article presents a narrative review of the literature on positive-affect-related forms of aggression and explores the role of aggression in eliciting positive affect across diverse social situations and relational contexts. An integrative model that unifies existing theories and findings is proposed, with the objective to inspire and inform future research.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".