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Crowd Sourcing: Do Peer Crowd Prototypes Match Reality?

Lilla K PivnickRachel A GordonRobert Crosnoe
Published in: Social psychology quarterly (2020)
During the transition into high school, adolescents sort large sets of unfamiliar peers into prototypical peer crowds thought to share similar values, behaviors, and interests (e.g., Jocks). Often, such sorting is based solely on appearance. This study investigates the accuracy of this sorting process in relation to actual characteristics using video and survey data from a longitudinal sample of U.S. youths who attended high school in the mid- to late-2000s. To simulate this sorting process, we asked same-birth-cohort strangers to view short videos of youths at age 15 and to classify those strangers into likely crowd membership. We then compared the classifications they made to how adolescents characterized themselves at that same time point. Results show that peer crowd classification predicts aspects of unknown peers' mental health, academic achievement, extracurricular involvement, social status, and risk-taking behaviors.
Keyphrases
  • high school
  • mental health
  • young adults
  • physical activity
  • healthcare
  • machine learning
  • deep learning
  • electronic health record
  • medical students
  • virtual reality