“I'm Okay. You're Drunk!” Self-Other Differences in the Perceived Effects of Alcohol in Real-Life Incidents of Aggression
Bibliographic record
Abstract
A general-population telephone survey of 1,753 Ontario adults was used to identify self-other differences in the perceptions of alcohol use, intoxication, and contributing effects of alcohol in incidents of physical aggression among the 152 respondents who had experienced such an incident in the past year. Respondents reported that opponents were significantly more intoxicated and that alcohol was more likely to have contributed to the opponent's aggression than to the respondent's. Self-other differences in the perceived role of alcohol remained significant when controlling for gender, usual drinking pattern of the respondent, whether only the opponent was physically aggressive, and whether the opponent was more intoxicated. These results suggest that people perceive themselves to be less affected by alcohol than their opponents are in incidents of aggression. This self-other difference is likely to influence both the escalation of aggression and the extent to which persons apportion responsibility to themselves and to others.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".