Violent Cognitions: Do Violent Offenders Express Evaluations, Norms, and Mitigations of Responsibility for Violence?
Bibliographic record
Abstract
Past research has suggested that the term attitudes has often been used as an umbrella term for a variety of cognitive constructs, including evaluations, norms, and mitigations.In order to better understand the nature of these cognitions, I identified statements from interviews with 44 violent offenders that seemed to reflect definitions for each cognition, and quantitized these statements according to their support or aversion to the use of violence.Inter-rater reliability analyses suggested that a second coder and I reliably identified each of these cognitions in ten randomly selected interviews, and that we reliably coded the valence of statements reflecting evaluations and norms.Correlations between these cognitive statements and two indices of violence were examined, and evaluations were associated with prior convictions for violence.These results suggest that theoretical conceptualizations of evaluations, norms, and mitigations correspond to how offenders talk about violence, and that evaluative statements were associated with violence.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".