The alcohol‐aggression relationship and differential sensitivity to alcohol
Bibliographic record
Abstract
Abstract The determination of which individuals are at risk of responding aggressively when intoxicated and under what conditions this is likely to occur is basic to understanding the alcohol/aggression relationship. Three theorized mechanisms on which individuals display differential vulnerability and which are related to risk are discussed. These are the cue for reinforcement system, the threat system, and the executive control system. Under the latter heading new findings from a number of studies are presented which demonstrate that: under low provocation intoxicated executive cognitive functioning (ECF) individuals performed with significantly more aggression than sober or intoxicated high ECF individuals; that individuals with low ECF, though more aggressive, choose these responses more slowly than those with high ECF; that low ECF, unlike high ECF, individuals do not react to anticipated shock; and, it is specifically low sober state ECF individuals who show increased alcohol induced ECF disruption who are most at risk for intoxicated aggression. Aggr. Behav. 29:302–315, 2003. © 2003 Wiley‐Liss, Inc.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".