In 2006, the Chilean government began the implementation of a “program of integrated childhood protection” called Chile Crece Contigo. This program involves the cooperation of the public health, education, and legal systems of Chile in the support of the Rights of the Child ratified by the United Nations in 1989, and guarantees accessibility to the diverse historical and cultural communities of Chile. This study seeks to evaluate the cultural pertinence of Chile Crece Contigo as implemented in the public health system of the predominantly Aymaran community of Putre and recommend policy changes and further investigation for the improvement of its implementation in all Aymaran communities of Northern Chile. These objectives were pursued through three weeks of observation of program activities as well as interviews with key implementers of Chile Crece Contigo in Putre’s Rural Family Health Center, a practitioner of traditional Aymaran medicine and mothers whose children are enrolled or were enrolled in a variety of the services offered by the program. These results highlighted successful efforts by health practitioners in Putre to provide culturally relevant care through Chile Crece Contigo, but also policy failures and ongoing uncertainty considering best practices for cultural pertinence within the program. The conclusion was reached that several funding and logistical policies of Chile Crece Contigo need to be altered to better serve this rural, Aymaran community. Also, several in-depth longitudinal studies on traditional Aymaran upbringing and the common socioeconomic conditions of the Aymaran community in Chile should be carried out in order to determine further policy changes and best practices for Chile Crece Contigo.

Artificial Intelligence’s facial recognition programs are inherently racially biased. The programs are not necessarily created with the intent to disproportionately impact marginalized communities, but through their data mining process of learning, they can become biased as the data they use may train them to think in a biased manner. Biased data is difficult to spot as the programming field is homogeneous and this issue reflects underlying societal biases. Facial recognition programs do not identify minorities at the same rate as their Caucasian counterparts leading to false positives in identifications and an increase of run-ins with the law. AI does not have the ability to role-reverse judge as a human does and therefore its use should be limited until a more equitable program is developed and thoroughly tested.

This survey takes information on a participant’s beliefs on privacy security, the general digital knowledge, demographics, and willingness-to-pay points on if they would delete information on their social media, to see how an information treatment affects those payment points. This information treatment is meant to make half of the participants think about the deeper ramifications of the information they reveal. The initial hypothesis is that this information will make people want to pay more to remove their information from the web, but the results find a surprising negative correlation with the treatment.

Consider Steven Cryos’ words, “When disaster strikes, the time to prepare has passed.” Witnessing domestic water insecurity in events such as Hurricane Katrina, the instability in Flint, Michigan, and most recently the winter storms affecting millions across Texas, we decided to take action. The period between a water supply’s disruption and restoration is filled with anxiety, uncertainty, and distress -- particularly since there is no clear indication of when, exactly, restoration comes. It is for this reason that Water Works now exists. As a team of students from diverse backgrounds, what started as an honors project with the Founders Lab at Arizona State University became the seed that will continue to mature into an economically sustainable business model supporting the optimistic visions and tenants of humanitarianism. By having conversations with community members, conducting market research, competing for funding and fostering progress amid the COVID-19 pandemic, our team’s problem-solving traverses the disciplines. The purpose of this paper is to educate our readers about a unique solution to emerging issues of water insecurity that are nested across and within systems who could benefit from the introduction of a personal water reclamation system, showcase our team’s entrepreneurial journey, and propose future directions that will this once pedagogical exercise to continue fulfilling its mission: To heal, to hydrate, and to help bring safe water to everyone.

This thesis explores the idea of the Natural Law by looking at the ideas of three thinkers: Aristotle, Thomas Aquinas and John Locke. It answers the question of what the Natural Law is, and further explains the derivative of this Natural Law, Natural Right. It further substantiates a relationship between the two. Finally, the thesis observes the prevalence these terms have in the American context, both in the Declaration of Independence as well as contemporary manifestations.

The Covid-19 pandemic has made a significant impact on both the stock market and the<br/>global economy. The resulting volatility in stock prices has provided an opportunity to examine<br/>the Efficient Market Hypothesis. This study aims to gain insights into the efficiency of markets<br/>based on stock price performance in the Covid era. Specifically, it investigates the market’s<br/>ability to anticipate significant events during the Covid-19 timeline beginning November 1, 2019<br/><br/>and ending March 31, 2021. To examine the efficiency of markets, our team created a Stay-at-<br/>Home Portfolio, experiencing economic tailwinds from the Covid lockdowns, and a Pandemic<br/><br/>Loser Portfolio, experiencing economic headwinds from the Covid lockdowns. Cumulative<br/>returns of each portfolio are benchmarked to the cumulative returns of the S&P 500. The results<br/>showed that the Efficient Market Hypothesis is likely to be valid, although a definitive<br/>conclusion cannot be made based on the scope of the analysis. There are recommendations for<br/>further research surrounding key events that may be able to draw a more direct conclusion.

The application of Toni Morrison’s Beloved as a lens through which one can analyze intergenerational trauma on an individual and communal level results in a blueprint towards a remedial process. The characters and their experiences in her novel are representative of a myriad of ways in which trauma is manifested. I have broken down the concept of intergenerational trauma into the idea that it can be seen as the state where one is both simultaneously “falling” and “fallen” at the same time. Used here, the term “falling” refers to the consistent, individual trauma that one is experiencing. On the other hand, the term “fallen” refers to the trauma that a community as a whole has experienced and internalized. This framework that I establish based off of Beloved is a launching point for the conversation surrounding the topic of remedial actions in relation to intergenerational trauma that resulted from slavery. Using it as a basis of knowledge allows one to truly gather the weight of the situation regarding trauma postbellum. Considering the current climate surrounding any meaningful dialogue, knowledge is one of the most important aspects. Along with the concepts of “falling”/”fallen,” I also coined the term productive memory, which refers to the act of confrontation as well as the remembering of intergenerational trauma. The use of productive memory is imperative in addressing the prior ideas presented regarding intergenerational trauma and the possible pathways to move forward.

The field of behavioral economics explores the ways in which individuals make choices under uncertainty, in part, by examining the role that risk attitudes play in a person’s efforts to maximize their own utility. This thesis aims to contribute to the body of economic literature regarding risk attitudes by first evaluating the traditional economic method for discerning risk coefficients by examining whether students provide reasonable answers to lottery questions. Second, the answers of reasonable respondents are subject to our economic model using the CRRA utility function in which Python code is used to make predictions of the risk coefficients of respondents via a two-step regression procedure. Lastly, the degree to which the economic model provides a good fit for the lottery answers given by reasonable respondents is discerned. The most notable findings of the study are as follows. College students had extreme difficulty in understanding lottery questions of this sort, with Medical and Life Science majors struggling significantly more than both Business and Engineering majors. Additionally, gender was correlated with estimated risk coefficients, with females being more risk-loving relative to males. Lastly, in regards to the model’s goodness of fit when evaluating potential losses, the expected utility model involving choice under uncertainty was consistent with the behavior of progressives and moderates but inconsistent with the behavior of conservatives.