Sexually Selected Preferences for Human Altruism Across Sexual Orientation, Gender, Age, and Reproductive Status

Prior studies have attempted to establish how human altruism has evolved, including theories of kin selection, reciprocal altruism, and costly signaling. Recent investigations have explored the evolution of altruism as the result of sexual selection, where individuals may exhibit altruistic behavior because it is preferred by potential mates. In this study, I examine how altruistic behavior toward different people (family, friends, strangers, or general altruistic acts) is preferred when considering potential short-term and long-term mates. While previous research has examined this question using college-aged heterosexual participants, this study uses a more diverse sample, including individuals who identify as LGBTQ, those of varying ages, and those who identify as childfree. Seven hypotheses were tested to understand how preferences for altruistic behavior vary based on individual characteristics. An on-line survey was conducted and over 500 participants responded. Results show that women prefer potential mates who behave altruistically toward strangers more so than men; when examining long-term relationships, people prefer potential mates who behave altruistically toward family; and that an individual’s self-reported altruistic behavior is positively correlated with an individual’s preference for altruistic behavior in a mate. Surprisingly, some hypotheses were not confirmed. For instance, there is no difference between preferences for altruistic behavior in potential mates based on sexual orientation. When examining women’s preferences for altruistic behavior in potential mates based on reproductive status, I found that post-reproductive women have a greater preference for altruistic behavior that is directed toward strangers or general altruistic behavior as compared to reproductive aged women. The results of this thesis provide insights into the evolution of human altruism

Social Networks and Instructional Reform in STEM: The Teaching-Research Nexus

Instructional reform in STEM aims for the widespread adoption of evidence based instructional practices (EBIPS), practices that implement active learning. Research recognizes that faculty social networks regarding discussion or advice about teaching may matter to such efforts. But teaching is not the only priority for university faculty – meeting research expectations is at least as important and, often, more consequential for tenure and promotion decisions. We see value in understanding how research networks, based on discussion and advice about research matters, relate to teaching networks to see if and how such networks could advance instructional reform efforts. Our research examines data from three departments (biology, chemistry, and geosciences) at three universities that had recently received funding to enhance adoption of EBIPs in STEM fields. We evaluate exponential random graph models of the teaching network and find that (a) the existence of a research tie from one faculty member i to another j enhances the prospects of a teaching tie from i to j, but (b) even though faculty highly placed in the teaching network are more likely to be extensive EBIP users, faculty highly placed in the research network are not, dimming prospects for leveraging research networks to advance STEM instructional reforms

Inductive Social Science Research as a Necessary Element of Data Science: Theory, Methods, and Scientific Convergence

Data science is a new scientific field that seeks to interrogate large and heterogeneous data sets in order to aid understanding of currently intractable phenomena. It is often characterized as a type of artificial intelligence. However, data science research often actually requires participation from knowledgeable and experienced qualitative researchers. In this presentation we will describe the work of qualitative social scientists (Anthropology undergraduate and Sociology undergraduate research assistants and a Sociologist tenured in a College of Engineering) collaborating with data scientists who are attempting to develop and validate models and algorithms that reliably predict criminal activity from the `Panama Papers database` and which can subsequently be generalized to perform similarly with other heterogeneous data sets. Data science models focus on financial traces that may indicate criminal behavior in the ‘Panama Papers’. Here, we will report specifically on our development and use of online ethnographic methods of research with a particular focus on understanding the social networks and culture of individuals identified in the Panama Papers data. This dual ‘data science and ethnographic‘ focus shows promise in supporting refinement data science models and algorithms

Validating Bad Entity Ranking in the Panama Papers via Open-Source Intelligence

The Panama Papers network maintained by the International Consortium of Investigative Journalists (ICIJ) represents a large set of relationships between people, companies, and organizations involved in the creation of offshore companies in tax-haven territories, mainly for hiding their assets. The Panama Papers network includes people or companies that had affairs with the Panamanian offshore law firm Mossack Fonseca, often with the purpose of laundering money. In our previous work, we proposed a ranking algorithm, namely the Suspiciousness Rank Back and Forth (SRBF) algorithm, that, given the Panama Papers network, leverages a blacklist of known bad entities to assign a degree of suspiciousness to each entity in the network. This algorithm proved to be efficient in detecting known bad entities in the Panama Papers, but we were not able to verify the accuracy of the produced entity ranking for non-blacklisted entities. In this paper, we propose to use the open-source intelligence (OSINT) methodology as a modern derivative of classical ethnographic and archaeological research methods that help us in validating with external open source data the ranking result of the Suspiciousness Rank Back and Forth algorithm. More specifically, we conduct a parallel, but independent, investigation using OSINT to assess the claims of SRBF algorithm. We identify positive outcomes from this study, describe current gaps in our process, and propose solutions to the gaps in order to better integrate the OSINT methodology with the SRBF ranking approach

Social Networks and Instructional Reform in STEM: The Teaching‑Research Nexus

Instructional reform in STEM aims for the widespread adoption of evidence based instructional practices (EBIPS), practices that implement active learning. Research recognizes that faculty social networks regarding discussion or advice about teaching may matter to such efforts. But teaching is not the only priority for university faculty – meeting research expectations is at least as important and, often, more consequential for tenure and promotion decisions. We see value in understanding how research networks, based on discussion and advice about research matters, relate to teaching networks to see if and how such networks could advance instructional reform efforts. Our research examines data from three departments (biology, chemistry, and geosciences) at three universities that had recently received funding to enhance adoption of EBIPs in STEM fields. We evaluate exponential random graph models of the teaching network and find that (a) the existence of a research tie from one faculty member i to another j enhances the prospects of a teaching tie from i to j , but (b) even though faculty highly placed in the teaching network are more likely to be extensive EBIP users, faculty highly placed in the research network are not, dimming prospects for leveraging research networks to advance STEM instructional reforms

The Telehealth Enhancement of Adherence to Medication (TEAM) in pediatric IBD trial: Design and methodology

Medication nonadherence is a significant health care issue requiring regular behavioral treatment. Lack of sufficient health care resources and patient/family time commitment for weekly treatment are primary barriers to receiving appropriate self-management support. We describe the methodology of the Telehealth Enhancement of Adherence to Medication (TEAM) trial for medication nonadherence in pediatric inflammatory bowel disease (IBD). For this trial, participants 11–18 years of age will be recruited from seven pediatric hospitals and will complete an initial 4-week run in to assess adherence to a daily medication. Those who take less than 90% of their prescribed medication will be randomized. A total of 194 patients with IBD will be randomized to either a telehealth behavioral treatment (TBT) arm or education only (EO) arm. All treatment will be delivered via telehealth video conferencing. Patients will be assessed at baseline, post-treatment, 3-, 6-, and 12-months. We anticipate that participants in the TBT arm will demonstrate a statistically significant improvement at post-treatment and 3-, 6-, and 12-month follow-up compared to participants in the EO arm for both medication adherence and secondary outcomes (i.e., disease severity, patient quality of life, and health care utilization). If efficacious, the TEAM intervention could be disseminated broadly and reduce health care access barriers so that patients could receive much needed self-management intervention

Learning from disease registries during a pandemic:Moving toward an international federation of patient registries

High-quality dermatology patient registries often require considerable time to develop and produce meaningful data. Development time is influenced by registry complexity and regulatory hurdles that vary significantly nationally and institutionally. The rapid emergence of the coronavirus disease 2019 (COVID-19) global pandemic has challenged health services in an unprecedented manner. Mobilization of the dermatology community in response has included rapid development and deployment of multiple, partially harmonized, international patient registries, reinventing established patient registry timelines. Partnership with patient organizations has demonstrated the critical nature of inclusive patient involvement. This global effort has demonstrated the value, capacity, and necessity for the dermatology community to adopt a more cohesive approach to patient registry development and data sharing that can lead to myriad benefits. These include improved utilization of limited resources, increased data interoperability, improved ability to rapidly collect meaningful data, and shortened response times to generate real-world evidence. We call on the global dermatology community to support the development of an international federation of patient registries to consolidate and operationalize the lessons learned during this pandemic. This will provide an enduring means of applying this knowledge to the maintenance and development of sustainable, coherent, and impactful patient registries of benefit now and in the future