The Roles of Empathy and Theory of Mind in Sexual Violence Perpetration: An Integrated Social Information Processing Framework
Bibliographic record
Abstract
Empathy is an aspect of social cognition which has gained much attention to explain sexual violence perpetration, with research resulting in mixed and contradictory findings. Another aspect of social cognition, which has been linked to, and even used synonymously with, cognitive empathy is Theory of Mind (ToM). However, research is severely lacking on the role which ToM may play in sexual violence. Hence, in order to elucidate such issues, following a theoretical discussion of these concepts, we propose an integrated Social Information Processing (SIP) framework to understand how empathy and ToM may be involved in sexual violence perpetration. This framework includes the steps laid out by Crick and Dodge's original SIP model, the emotional processes emphasised by Lemerise and Arsenio, the empathy stages identified by Marshall and colleagues, and our propositions concerning the role played by ToM. In this theoretical article, we argue that ToM and empathy are primarily involved in earlier SIP stages, but may have repercussions on subsequent stages as SIP is cyclical. Nonetheless, greater research is needed to conclusively identify where deficits lie. In terms of clinical implications, we recommend that empathy and ToM be targeted in interventions (primary, secondary and tertiary) using cognitive-behavioural therapy principles which are involved in, and affect, SIP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".