Provoking Punches: Factors Influencing Perceived Violent Reactions to Negative Situations
Bibliographic record
Abstract
Purpose: Violence among college students is an important area of research as this group is at an increased risk of both engaging in and being a victim of violence. As such, the current research aimed to examine factors that may influence violent tendencies among a sample of college students. Method: Data from 101 completed surveys were analyzed. Principal components factor analysis and Cronbach’s alpha resulted in the creation of six independent variables (gun experience, weapons support, anger contagion, witness violence, violent community, and aggressive beliefs) and four dependent variables (competition for resources, social attacks, physical attacks, and unfair situations). OLS regression was used to estimate the impact of each variable on perceptions of reacting with violence to four negative situations. Results: Gun experience and violent community significantly predicted responding violently to both social and physical attacks, while gun support was only predictive of violence in competition for resources. Additionally, aggressive beliefs predicted perceptions of violent responses to physical attacks and in unfair situations. Finally, anger contagion was associated with students reporting an increased likelihood of responding violently to social attacks. Conclusions: While research shows the importance of understanding violence exposure and aggressive norms in creating and improving violence prevention programs and anti-violence strategies, the role that perceptions play is largely absent. Furthermore, this research supports the importance of implementing these programs and strategies among college students/young adults to potentially reduce violence and aggression within this age group.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".