The Relationship Between Attitudes Towards Violence and Violent Behaviour: The Use of Implicit and Self-Report Measures
Bibliographic record
Abstract
The current studies investigated the relationship between attitudes towards violence and violent behaviour.Violent attitudes have mostly been assessed with self-report measures.Within social psychology, implicit attitudes have also been assessed using response latency measures finding significance regarding these attitudes.The current studies examined implicit and self-report attitudes, as well as the relationship between attitudes and past/future violence, among three studies (one containing offenders).The effect of observing, or engaging in, violence on attitudes and whether this affects the relationship with violent behaviour was also examined.No significant results were found involving implicit attitudes; however self-report attitudes were positively related to measures of violent behaviour and more positive self-report attitudes were found after observing, or engaging in, the violent task, as was a positive relationship between these attitudes and future violence.These results extend previous research and provide valuable information regarding the role of attitudes in the commission of violence.Keywords: implicit, explicit, self-report, attitudes, violence, violent behaviour, exposure to violence How often do you usually play Wii Tennis?(once a day, once a week, once a month, once every two months, once every three months, once every four months, once every five months, once every six months or less, never) How often do you usually play any Wii games?(once a day, once a week, once a month, once every two months, once every three months, once every four months, once every five months, once every six months or less, never) How often do you play violent video games of any kind (Wii, X-Box, etc.)? (once a day, once a week, once a month, once every two months, once every three months, once every four months, once every five months, once every six months or less, never)
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".