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- Creators: Sandra Day O'Connor College of Law
- Creators: Funk, Kendall
The right to cast a meaningful vote, equal in value to other votes, is a fundamental tenet US elections. Despite the 1964 Supreme Court decision formally establishing the one person, one vote principle as a legal requirement of elections, our democracy consistently falls short of it. With mechanisms including the winner-take-all format in the Electoral College, disproportioned geographic allocation of senators, extreme partisan gerrymandering in the House of Representatives, and first-past-the-post elections, many voters experience severe vote dilution. <br/><br/>In order to legitimize our democratic structures, American elections should be reformed so every person’s vote has equal weight, ensuring that the election outcomes reflect the will of the people. Altering the current election structure to include more proportional structures including rank choice voting and population-based representation, will result in a democracy more compatible with the one person, one vote principle.
This paper conducts an exploration of the election policy reaction to the COVID-19 pandemic within the United States. While living through and voting during the real-time events which took place during the COVID-19 Pandemic of 2020, it soon became evident that there was not enough experience from earlier election emergencies to properly ensure against voter disenfranchisement. Given the scope of the global pandemic and the speed with which policymakers had to act, there was very little time to properly prepare. There was also great contention regarding the legitimacy of election methods proposed to alleviate in-person election concerns, such as mail-in voting. The political battle between those who believed COVID-19 to be a grave concern against those who did not consider COVID-19 to be a legitimate threat towards their livelihoods also affected policymaking decisions. Policymakers were forced into a corner, as they experienced criticism for not enough government action, as well as disapproval on the actual regulation that came to pass. This paper therefore aims to understand what factors led to the decisions which shaped the election policy which occurred as a reaction to the COVID-19 pandemic during the election year of 2020. This analysis is conducted by considering the following: prior election emergency policy; the development of reactive election policy in March, proactive policy established for the August and November elections; and a review of voter disenfranchisement which occurred due to COVID-19.
With a prison population that has grown to 1.4 million, an imprisonment rate of 419 per 100,000 U.S. residents, and a recidivism rate of 52.2% for males and 36.4% for females, the United States is facing a crisis. Currently, no sufficient measures have been taken by the United States to reduce recidivism. Attempts have been made, but they ultimately failed. Recently, however, there has been an increase in experimentation with the concept of teaching inmates basic computer skills to reduce recidivism. As labor becomes increasingly digitized, it becomes more difficult for inmates who spent a certain period away from technology to adapt and find employment. At the bare minimum, anybody entering the workforce must know how to use a computer and other technological appliances, even in the lowest-paid positions. By incorporating basic computer skills and coding educational programs within prisons, this issue can be addressed, since inmates would be better equipped to take on a more technologically advanced labor market.<br/>Additionally, thoroughly preparing inmates for employment is a necessity because it has been proven to reduce recidivism. Prisons typically have some work programs; however, these programs are typically outdated and prepare inmates for fields that may represent a difficult employment market moving forward. On the other hand, preparing inmates for tech-related fields of work is proving to be successful in the early stages of experimentation. A reason for this success is the growing demand. According to the U.S. Bureau of Labor Statistics, employment in computer and information technology occupations is projected to grow 11 percent between 2019 and 2029. This is noteworthy considering the national average for growth of all other jobs is only 4 percent. It also warrants the exploration of educating coders because software developers, in particular, have an expected growth rate of 22 percent between 2019 and 2029. <br/>Despite the security risks of giving inmates access to computers, the implementation of basic computer skills and coding in prisons should be explored further. Programs that give inmates access to a computing education already exist. The only issue with these programs is their scarcity. However, this is to no fault of their own, considering the complex nature and costs of running such a program. Accordingly, this leaves the opportunity for public universities to get involved. Public universities serve as perfect hosts because they are fully capable of leveraging the resources already available to them. Arizona State University, in particular, is a more than ideal candidate to spearhead such a program and serve as a model for other public universities to follow. Arizona State University (ASU) is already educating inmates in local Arizona prisons on subjects such as math and English through their PEP (Prison Education Programming) program.<br/>This thesis will focus on Arizona specifically and why this would benefit the state. It will also explain why Arizona State University is the perfect candidate to spearhead this kind of program. Additionally, it will also discuss why recidivism is detrimental and the reasons why formerly incarcerated individuals re-offend. Furthermore, it will also explore the current measures being taken in Arizona and their limitations. Finally, it will provide evidence for why programs like these tend to succeed and serve as a proposal to Arizona State University to create its own program using the provided framework in this thesis.