Attitudes toward sexual violence and victim-blaming are culturally dependent and should be examined within specific social and legal contexts. The present study sought to compare Israeli police officers' (N = 220) and students' (N = 230) perceptions toward sex working rape victims. Participants were presented with a vignette describing a rape, where the victim was either identified as a sex worker or not. They then provided ratings of blame and sentencing, along with assessments of victim resistance and harm and their emotional responses toward the victim. Significant differences emerged between the groups. Although overall victim-blaming was relatively low, police officers attributed more blame when the victim was identified as a sex worker, while students' blame attribution remained unaffected by the victim's status. Similarly, police officers were more lenient toward the perpetrator than students regarding punishment, particularly when the victim was identified as a sex worker. These differences also appeared in emotional responses and evaluations of victim resistance and harm, indicating that police officers held more stereotypical and skeptical attitudes compared to students. The findings suggest that law enforcement attitudes may reflect a unique institutional perspective, shaped by organizational norms within the police force, the broader legal discourse, and police officers' work-related exposure.
{"title":"Sex Work and Sexual Victimization: A Comparative Study of Students’ and Police Officers’ Perceptions of Sex-Working Rape Victims","authors":"Liza Zvi, Mally Shechory Bitton","doi":"10.1002/bsl.70024","DOIUrl":"10.1002/bsl.70024","url":null,"abstract":"<p>Attitudes toward sexual violence and victim-blaming are culturally dependent and should be examined within specific social and legal contexts. The present study sought to compare Israeli police officers' (<i>N</i> = 220) and students' (<i>N</i> = 230) perceptions toward sex working rape victims. Participants were presented with a vignette describing a rape, where the victim was either identified as a sex worker or not. They then provided ratings of blame and sentencing, along with assessments of victim resistance and harm and their emotional responses toward the victim. Significant differences emerged between the groups. Although overall victim-blaming was relatively low, police officers attributed more blame when the victim was identified as a sex worker, while students' blame attribution remained unaffected by the victim's status. Similarly, police officers were more lenient toward the perpetrator than students regarding punishment, particularly when the victim was identified as a sex worker. These differences also appeared in emotional responses and evaluations of victim resistance and harm, indicating that police officers held more stereotypical and skeptical attitudes compared to students. The findings suggest that law enforcement attitudes may reflect a unique institutional perspective, shaped by organizational norms within the police force, the broader legal discourse, and police officers' work-related exposure.</p>","PeriodicalId":47926,"journal":{"name":"Behavioral Sciences & the Law","volume":"44 1","pages":"109-119"},"PeriodicalIF":1.3,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12865668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145640924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines how secondary victimization is interactionally produced during courtroom cross-examinations of women who have experienced sexual violence. Drawing on Ethnomethodology, Conversation Analysis and Membership Categorization Analysis, the study investigates how defense attorneys invoke rape myths and gendered stereotypes to challenge victims' credibility and moral character. Using the extracts of two cross-examinations from the celebrity trial CA v. Winslow II (2019), the results highlight how interactional features of questioning reproduce cultural assumptions that legitimate secondary victimization, constructing victims as unreliable or complicit. The findings highlight the “double bind” faced by women in sexual assault trials: they must appear both emotionally credible and rationally composed to be believed, yet any deviation from this ideal invites disbelief. Methodologically, the paper underscores the underutilized potential of EMCA in legal-linguistic research to reveal how institutional talk reproduces gendered injustice through ordinary conversational practices.
本文探讨了在法庭上对经历过性暴力的妇女进行质证时,二次受害是如何相互作用产生的。利用民族方法学、对话分析和成员分类分析,该研究调查了辩护律师如何利用强奸神话和性别刻板印象来挑战受害者的可信度和道德品质。使用名人审判CA v. Winslow II(2019)的两次交叉询问的摘录,结果突出了质疑的相互作用特征如何再现了使二次受害合法化的文化假设,将受害者构建为不可靠或同谋。这些发现凸显了女性在性侵犯审判中面临的“双重困境”:她们必须在情感上可信,在理性上沉着冷静,才能让人相信,但任何偏离这一理想的行为都会引起怀疑。在方法上,本文强调了EMCA在法律语言学研究中未被充分利用的潜力,以揭示制度谈话如何通过日常会话实践再现性别不公正。
{"title":"The “Double Bind” of Gender-Based Violence: Secondary Victimization in Courtroom Cross-Examinations","authors":"Selena Mariano","doi":"10.1002/bsl.70025","DOIUrl":"10.1002/bsl.70025","url":null,"abstract":"<p>This paper examines how secondary victimization is interactionally produced during courtroom cross-examinations of women who have experienced sexual violence. Drawing on Ethnomethodology, Conversation Analysis and Membership Categorization Analysis, the study investigates how defense attorneys invoke rape myths and gendered stereotypes to challenge victims' credibility and moral character. Using the extracts of two cross-examinations from the celebrity trial <i>CA v. Winslow II</i> (2019), the results highlight how interactional features of questioning reproduce cultural assumptions that legitimate secondary victimization, constructing victims as unreliable or complicit. The findings highlight the “double bind” faced by women in sexual assault trials: they must appear both emotionally credible and rationally composed to be believed, yet any deviation from this ideal invites disbelief. Methodologically, the paper underscores the underutilized potential of EMCA in legal-linguistic research to reveal how institutional talk reproduces gendered injustice through ordinary conversational practices.</p>","PeriodicalId":47926,"journal":{"name":"Behavioral Sciences & the Law","volume":"44 1","pages":"96-108"},"PeriodicalIF":1.3,"publicationDate":"2025-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12865666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145588768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This pilot study compares offender risk assessments conducted by human experts and advanced large language models (LLMs) within the HCR-20V3 framework. Both groups evaluated a series of synthetic forensic case vignettes designed to simulate realistic clinical conditions. Quantitative results indicate that AI models consistently assigned higher overall risk scores and demonstrated greater inter-rater reliability compared to human assessors. Qualitative analysis revealed distinct reasoning patterns: AI systems emphasized historical and static risk factors and often recommended more intensive management strategies, whereas human experts focused on recent behavioral improvements, dynamic change, and rehabilitation potential. These contrasts highlight fundamental differences between algorithmic pattern recognition and human clinical judgment. The findings suggest that integrating AI-generated analyses with professional expertise can enhance the consistency and transparency of risk evaluations, while preserving the ethical, contextual, and human-centered insights essential to forensic and clinical decision-making.
{"title":"Human Experts and AI Models in Offender Risk Assessment: A Comparative Pilot Study Using the HCR-20V3","authors":"Shai Farber","doi":"10.1002/bsl.70023","DOIUrl":"10.1002/bsl.70023","url":null,"abstract":"<p>This pilot study compares offender risk assessments conducted by human experts and advanced large language models (LLMs) within the HCR-20V3 framework. Both groups evaluated a series of synthetic forensic case vignettes designed to simulate realistic clinical conditions. Quantitative results indicate that AI models consistently assigned higher overall risk scores and demonstrated greater inter-rater reliability compared to human assessors. Qualitative analysis revealed distinct reasoning patterns: AI systems emphasized historical and static risk factors and often recommended more intensive management strategies, whereas human experts focused on recent behavioral improvements, dynamic change, and rehabilitation potential. These contrasts highlight fundamental differences between algorithmic pattern recognition and human clinical judgment. The findings suggest that integrating AI-generated analyses with professional expertise can enhance the consistency and transparency of risk evaluations, while preserving the ethical, contextual, and human-centered insights essential to forensic and clinical decision-making.</p>","PeriodicalId":47926,"journal":{"name":"Behavioral Sciences & the Law","volume":"44 1","pages":"87-95"},"PeriodicalIF":1.3,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12865665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145497042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}