Witness Cross-Examinations in Non-Stranger Assault Crimes: An Appraisal Analysis

• Autor(a) principal: Gales, Tammy
• Data de Publicação: 2017
• Outros Autores: Solan, Lawrence M.
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://ojs.letras.up.pt/index.php/LLLD/article/view/3286
• topic: Articles

“It has been said that the victim of a sexual assault is actually assaulted twice – once by the offender and once by the criminal justice system2”. In the U.S. court system, the principal player is the defense lawyer who crossexamines the complaining witness. The literature to date has not linguistically examined the extent to which the actual questioning differs from questioning in similar cases. Thus, we ask: other than the fact that the crime is so personal, what is different about the language of sexual and non-sexual assault trials? We obtained one transcript of three types of trial (sexual assault by a male on a female; non-sexual assault by a male on a female; and non-sexual assault by a male on a male) and analyzed the cross-examinations using Appraisal Analysis (Martin and White, 2005), which identifies a speaker or writer’s stance toward another person or proposition. The findings revealed similar strategies; however, while all three lawyers effectively and legally discredit the witness, there are subtle differences in the judgements they imply. Of course, we cannot generalize the findings from these three cases to claim distinctions between the cross-examination strategies used in sexual and non-sexual assault cases. However, our main goal in this initial study is to demonstrate the usefulness of Appraisal Analysis as a tool for achieving a more nuanced understanding of the use of stance markers during cross-examination. It may be that it is this strategy that inhibits many women from pursuing rape cases in court; if so, Appraisal Analysis would be a useful tool for future research on a larger data set.