Graduation Year

2026

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Criminology

Major Professor

Joan A. Reid, Ph.D., LMHC

Committee Member

Fawn Ngo, Ph.D.

Committee Member

Mateus Renno Santos, Ph.D.

Committee Member

Ieke de Vries, Ph.D.

Keywords

critical race theory, gendered pathways, youth victimization, intersectionality

Abstract

Youth and human trafficking remain significant and complex issues. Existing literature suggests that trafficking risk, victimization, and identification are shaped by structural and social context, creating differential impacts across demographic groups, such as racial, ethnic, and gendered groups. Yet, current research has yet to fully capture the complexity of victimization across groups. This dissertation consists of three interconnected studies examining youth trafficking investigations. Using a mixed-methods approach and administrative data from Florida Department of Children and Families (DCF), this dissertation address three objectives (1) examining potential variation in documentation of trafficking risk, victimization, and narrative framing of youth trafficking investigations across racial groups, (2) identifying distinct profiles of potentially trafficked youth and assessing for variation in class membership across race, ethnicity, and gender, and (3) evaluating the predictive validity of a human trafficking screening instrument across race, ethnicity, and gender. Qualitative findings revealed similarities in documented risks of youth trafficking across Black and White youth, though key differences emerged, such as higher levels of system involvement for Black youth trafficking victims. Results also revealed evidential circumstances surrounding case substantiation and variation in investigative narratives across White and Black youth. Latent class analysis identified multiple distinct profiles of potentially trafficked youth, characterized by sexual indicators, behavioral indicators, and low levels of documented trafficking indicators, demonstrating that trafficking contexts are not uniform and variation in clusters of indicators exists across demographic groups, suggesting trafficking victimization manifests through multiple pathways across groups. Lastly, machine learning analysis indicates that while core indicators are consistent, screening tool performance differs across demographic populations, with reduced identification accuracy for certain demographic groups. Collectively, these findings challenge universal approaches to trafficking research and identification and demonstrate the need to consider social contexts in anti-trafficking efforts.

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