Negotiating Hegemonic Masculinities and Mutual Exoticization within Grindr Tourism Interactions: "Everyone in Tel Aviv is a Muscle God".

Dating apps are spaces where masculinities are communicated digitally, affecting inequalities around gender expressions. This study aims to identify the masculinities communicated within Grindr tourism interactions in Tel Aviv and analyze how masculinities shape relations among international tourists and locals. Methods consist of interviews and audio diaries with nineteen tourist, local, and immigrant Grindr users in Tel Aviv. Theories of affordances, hegemonic masculinities, and relational sociology are used to analyze the data. In discussion of the results, the concept of mutual exoticization is put forth to interpret the social dynamics valued within Grindr tourism interactions, thereby revealing oppositional fantasies tied to nationalities and masculinities. The study also explores ethnicity's association with social and erotic capital in digital circulations of a localized Mizrahi masculinity. Findings reveal that while hegemonic masculinities are circulated on Grindr, resistance is also a part of Grindr tourism relations around masculinities. This research concludes by shedding light on masculinity inequalities within digital spaces where different nationalities co-congregate, revealing everyday negotiations of gender regimes. Grindr is the site of both reproduction of and resistance to social inequalities around masculinities.

A Framework for Modeling Emerging Diseases to Inform Management.

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.