JOURNAL ARTICLE

Sexual Scripts and Sexual Consent: Gender Stereotypes, Music-Media Messages, and Sexual Consent Expectancies Among College Men and Women.

  • Published In: Journal of Interpersonal Violence, 2023, v. 38, n. 15/16. P. 9264 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Rodgers, Kathleen Boyce; Hust, Stacey J. T.; Li, Jiayu; Kang, Soojung; Garcia, Ariana L. 3 of 3

Abstract

This article examines how personal experiences with sexual violence, gendered beliefs, and perceptions of women in music media influence sexual-consent expectancies among primarily White heterosexual college students at a northwestern U.S. university (n = 364). Using sexual scripting theory as a framework, the study found that individuals with a history of sexual perpetration, stronger sexual stereotypes, or who perceived women in music videos as powerless reported lower expectancies to seek consent, refuse unwanted sexual advances, or adhere to a partner's consent decisions. Notably, men with past perpetration histories showed particularly low expectancies to adhere to consent decisions, while women endorsing traditional sexual stereotypes had lower expectancies to seek consent and refuse unwanted sex. The findings suggest that gendered sexual scripts reinforced by media portrayals contribute to sexual consent negotiation challenges, highlighting the potential of media literacy and prevention programs to address these scripts and reduce sexual violence on college campuses.

Additional Information

  • Source:Journal of Interpersonal Violence. 2023/08, Vol. 38, Issue 15/16, p9264
  • Document Type:Article
  • Subject Area:Sociology
  • Publication Date:2023
  • ISSN:0886-2605
  • DOI:10.1177/08862605231165766
  • Accession Number:164615741
  • Copyright Statement:Copyright of Journal of Interpersonal Violence is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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