Telling it Like it isn’t: Obscuring Perpetrator Responsibility for Violent Crime
Bibliographic record
Abstract
Part I of this article introduces the interactional and discursive view of violence and resistance, part II illustrates its application to the analysis of sexual assault trial judgments, and part III provides a detailed analysis of an entire judgment. In giving their reasons for verdicts and sentences, the majority of judges accounted for the assaults by drawing on psychological concepts and constructs. These psychological explanations or causal attributions were grouped into one or more of eight categories: alcohol and drug abuse, biological or sexual drive, psychopathology, dysfunctional family upbringing, stress and trauma, character or personality trait, emotional state, and loss of control. The causal attributions in all categories systematically reformulated deliberate acts of violence into non-deliberate and non-violent acts. Psychologizing attributions, that is, causal attributions that functioned to conceal the violence and mitigate the perpetrator’s responsibility, accounted for 97 percent of attributions. Through line-by-line analyses of the full text of one judgment, we show how psychologizing attributions are combined in use with other linguistic devices to (i) conceal violence, (ii) mitigate perpetrators’ responsibility, (iii) conceal victims’ resistance, and (iv) blame or pathologize victims.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".