No abstract

In 2017, the Australian Government announced that a voluntary postal survey would be used to quantify the views of the Australian public on marriage equality. This non-binding, voluntary postal survey-and the associated public debate-can be viewed as a discriminatory event for same-sex attracted Australians. The exacerbation of minority stress likely imposed by this unexpected event has resulted in an unprecedented demand for psychological services by members of this community. Despite this surge of use, relatively little is known about the specifics of the impact of this discriminatory event. Method: In this article, we present the findings of a thematic analysis of semistructured interviews that qualitatively examined the impact of the marriage equality debate among a sample of 14 Australians (eight sexual minority and six affected 'allies'). Results: Two themes were identified from the interviews, each with four subthemes: (a) personal impacts (emotional wellbeing, empathic concern, devaluation, and connection to religion), and (b) social impacts (activism, avoidant behaviour, social connections, and societal perceptions). Conclusions: Overall, the findings of the current study reveal a range of intraand inter-personal negative impacts of public debate about the equal rights of same-sex attracted people to marry. Moreover, the results suggest that the impact is not only on this minority and at-risk group but also on their heterosexual allies. These results can help inform future policy with the aim of decreasing minority stress experienced by same-sex attracted people.

This study aimed to gauge if adolescents' bias or prejudice towards a particular gender could be observed through narrator preference in auditory advertisements to ascertain if the perception of gender and its stereotypes has changed among younger generations. Prior research shows that when adult subjects are presented with multiple advertisements that they demonstrate a preference towards male narrated advertisements; however, these previous studies were performed on adults; therefore, narrator preference remains unknown for most teenagers. For this study, research data were collected through a mixed media survey in which a descriptive research process was completed. Participants in this study included 135 high school juniors and seniors both male and female. Initial results showed that statistically there was no preference for either male or female narration. From this data, one can conclude that today's teenagers do not show an overt bias for a narrator of a specific gender. Therefore, the conclusion can be drawn that the perception of gender and gender stereotypes have changed towards more egalitarian views in today's younger generations. However, this study was limited to high school-aged teenagers and did not encompass youth of all age groups. Future research should compare perceived gender stereotypes among various age groups to identify a more precise pattern of generational change of gender perception.

The purpose of this study is to gain an understanding of the impact of model architecture on the efficacy of adversarial examples against machine learning systems implemented in self-driving applications. Prior research shows how to create and train against adversarial examples in many use cases; however, there is no definite understanding of how a machine learning model’s architecture affects the efficacy of adversarial examples. Data was collected through an experimental setting involving end-to-end self-driving models trained through behavioral cloning. Three model types were tested based on popular frameworks for machine learning algorithms dealing with images. Results showed a statistically significant difference in the impact of adversarial examples between these models. This means that certain model types and architectures are more susceptible to attacks. Therefore, the conclusion can be made that model architecture does impact the efficacy of adversarial examples; however, this is potentially limited to closed-loop, end-to-end systems in which algorithms make the entire decision. Future research should investigate what specific structure within models causes increased susceptibility to adversarial attacks.