Nombres trigonométriques au cours de physique

peer reviewedL'article, issu d'un travail d'étudiant, propose la résolution d'exercices issus du cours de physique et faisant intervenir les nombres trigonométriques. La description de la résolution devrait permettre à l'enseignant de mathématiques qui le souhaite d'utiliser ces exercices comme application des relations dans le triangle rectangle

Factors that Affect Perceptions of Gig-Workers

According to Pew Research Center, 16% Americans have earned money on online gig platforms such as Uber or TaskRabbit at some point. However, research on how consumers select gig workers is still mostly underexplored. The purpose of this study is to investigate the potential impact of a gig worker’s gender and self-presentation in their profile picture on consumer perceptions and choices. Specifically, we propose that smiling and quality of profile picture in terms of professionalism positively influence consumers’ perceptions on the gig worker’s competence, trustworthiness, and the likelihood of hiring them for a task. We also propose that these two factors will interact with gender, such that the positive effect of smiling is greater for female than for male workers, and the positive effect of professionalism is greater for male than for female workers. Lastly, we hypothesize that gender bias exists on gig platforms, such that male and female gig workers are more likely to be selected for tasks that are stereotypically aligned with their traditional gender roles. We conducted a 2 (gender: male vs. female) x 2 (smile: smiling vs. neutral) x 2 (professionalism: professional headshot vs. selfie) between-subjects experiment on Qualtrics to test our hypotheses. Eight fake worker profiles were created with different profile pictures to reflect the experimental manipulation. Undergraduate psychology students took an online survey where they were randomized to view one of the eight worker profiles. Additionally, we created one more worker profile that showed a smiling male in a professional headshot, which served as a comparison in each condition. We are currently in the process of data collection, which will be completed by December 2022. Data analyses will be completed by March 2023. Our findings will provide practical implications to gig workers regarding how to enhance their chance of being selected