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Past research has constructed a medicalized model of trans women’s sexuality, where trans women are believed to be hyposexual and distressed by their bodies pre-transition, and are cured of their sexual dysfunction as a result of gender affirmative medical procedures. The current study engaged a community sample (N = 169) of trans feminine and nonbinary individuals assigned male at birth (TFNB) to investigate predictors of sexual experiences after addressing methodological biases of prior studies, including body satisfaction (using a modified Body Image Scale) and social contextual factors. Hierarchical regressions were conducted to test the hypothesis that after accounting for demographic variables and social contextual aspects (i.e., body satisfaction, social dysphoria, and fetishization), medical transition (i.e., hormone therapy) would not significantly predict five outcomes of sexual experience (i.e., receptive penetration, insertive penetration, importance of sex, sexual pleasure, and sexual intimacy). Across all models, medical transition was not a significant predictor of sexual experiences; however, sexual orientation, age, body satisfaction, and experiences of fetishization were frequent predictors. Results suggest that the sexual experiences of TFNB individuals do not align with the medicalized model and that demographic and contextual factors play an important role in the sexual outcomes for this population.

Item does not contain fulltext, Algorithmic decision-making systems are becoming increasingly prominent in the mobility sector through navigation systems, autonomous driving vehicles, infrastructure management and even through their implementation in customer services. However, the advancements in mobility will only be successful if they are accepted and adopted by the majority of the public. In this paper, we test the perception of public towards algorithmic decision-making systems and their willingness to delegate the task within the mobility sector using a factorial survey approach. Unlike the standard one-factor-at-a-time survey analysis, factorial survey gives us an opportunity to test the perception of trust through various dimensions including personality, task and algorithm related factors, spread over different levels. For example, each participant is given a series of scenarios consisting of a combination of dimensions; with every new scenario in the series, the levels of the dimensions are changed. This allows us to reduce internal biases of the participants by affiliating them to the scenario and thus increasing the internal and external validity of our results. Our results indicate that consumers are less algorithm averse when they have more information about the algorithm (increased transparency), when they have some control over the algorithm, when the algorithm has higher accuracy in performing the task and when it is characterized by the ability to learn. Our findings could act as a starting point for a discussion on ways in which consumer trust in algorithmic decision-making systems can be improved.

18 augustus 2022, Contains fulltext : 252826.pdf (Publisher’s version ) (Open Access)