Breaking the link between provocation and aggression: The role of mitigating information
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Bibliographic record
Abstract
In two experimental studies, we examine the extent to which strong or weak mitigating information after a provocation alters aggressive responding. In Study 1, we randomly assigned 215 (108 female) college-aged participants to a strong or weak provocation by having a research assistant talk to the participant about failing a task in a harsh or confused tone. This was followed by a second research assistant giving a strong or weak excuse to the participant regarding the first research assistant's behavior. Then, aggressive behavior was assessed using a researcher rating task. In Study 2, 63 (25 female) college-aged participants interacted with a confederate on the CRT. All participants were strongly provoked by receiving strong noise blasts. After five CRT trials, the confederate delivered weak or strong mitigating information to the participant regarding the noises blasts. The results indicated that: (i) strong provocations are more likely to increase aggression than weak provocations; (ii) strong mitigating information is more likely to decrease aggression than weak mitigating information; and (iii) the varying strength of mitigating information is important in situations involving weak, but not strong provocations: strong mitigating information is more likely than weak mitigating information reduce aggression when provocation is strong, but not when provocation is weak. We discuss the importance of mitigating information in decreasing aggressive behavior and the conditions in which mitigating information is especially likely to be effective. Aggr. Behav. 42:555-562, 2016. © 2016 Wiley Periodicals, Inc.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it