“The way I see it makes me believe you intentionally did it”: Intentionality ascription and gaze transition entropy in violent offenders
Bibliographic record
Abstract
Cognitive processes underlying inferences regarding inferring mental states (i.e. intentionality ascription) are still to be investigated. To assess how people accumulate social cues in order to attribute intentionality, a measure of gaze transition entropy (GTE) seems indicated to throw some light on these processes. Violent behavior is associated with distorted attributional processes but also with deficiencies in attention to socially relevant cues. Therefore, the current study compared the level of entropy between both violent male and female offenders and non-offenders and explore the association between GTE and ascribing intentionality. The sample (N = 128) consisted of violent inmates (N = 63, 31 women) and adults living in the community (N = 65, 31 women). Lower entropy characterized violent offenders to a greater extent as compared to those with no history of volent crimes. Moreover, lower entropy predicted greater intentionality ascription especially in judging ambiguous and hostile harmful events but only in the violent offender group. Findings imply that hostile attributions in violent offenders not only depend on a predisposition to interpret external reality in a hostile manner but can be the result of an inferential processing based on insufficient and incomplete information.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".