Recent Submissions

  • COVID-19 And Unemployment Rate

    Ardon, Kenneth; De Oliveira, Brandon (2024-05-01)
    Covid-19 affected the unemployment rate in the United States to an enormous extent which has crippled the economy. Covid-19 contributed to worsening the factors that contribute to the unemployment rate. These factors in themselves are the combination of independent variables that influence the average citizen or are the results of their decisions. This information is important to understand for the economic/financial impact for the United States in future scenarios. This paper hypothesizes that the desire to be safe from a virus in a population will be a large contributor to the unemployment rate. The desire to be safe is represented by those that never or rarely use their masks when they go outside. The most direct method of Covid’s effect on the market being the death rate of Covid. While unemployment rate’s other various factors are involved as well, examples being Trump voters, populations age, and education. The years of these variables will be from 2020 and 2022 in order to compare the lasting impact. From the population of the US this information will be taken from by county. All of the numerical data is organized in excel, where regressions are made amongst factors. This constant use of regression is to prove the significance of connection between Covid-19 and unemployment rate. Alternatively, some of these factors were very related to one another and must be identified to not confuse the ultimate regression. Results have shown political alignment of Trump voters' correlation between the unemployment rate compared to the others. With the desire to be safe having little effect compared to Trump voters. The implications of this shows that in order to help combat future cases of a Pandemic of deadly disease, the political leanings of the United States should be a bit more united.
  • Critique Of The Interpretations And Prospects Of Artificial Intelligence

    Marrero, Danny; Heffner, Rhaea (2022-05-01)
    The field of Artificial Intelligence has had a particular creative and productive period in recent years, drawing attention and further participation in its development from both researchers and business. Artificial Intelligence is said to be making progress on key benchmark tests and researchers are producing impressive and surprising machines. What interests us primarily as philosophers of Artificial intelligence is the interpretive meaning we give to these advances to the field, and whether human level general intelligence could ever be reproduced in a machine. In this paper, there will be a review of the dominant paradigms in the philosophy of artificial intelligence and various critique of their theories of mind and the views of artificial intelligence research and why they fail. There will be a proposal of a better meaning of intelligence and the Lovelace Test of machine intelligence that better encapsulates this meaning of intelligence. Lastly, there will be a demonstration of why serial computers have and will never pass the Lovelace Test and therefore never be intelligent.
  • College Major and the Gender Pay Gap

    von Seekamm, Kurt; Gentile, Isabella (2021-05-01)
    Using the IPUMS USA database and the American Community Survey sample for the year of 2018, this paper seeks to explain how graduating with a bachelor’s degree in a female dominated major can affect post-graduation earned income. Increases in the percentage of female students within a field of study have negative effects on an individual’s earned income. Even after controlling for the percentage of female students within a degree field, there is an additional penalty to one’s income for working within a female dominated industry.
  • How Does Teacher Retention Affect Student Achievement?

    Ardon, Ken; Rivera, Tatiana (2019-05-01)
    This paper attempts to analyze the impact that teacher retention has on student achieving. This study estimates the effects of teacher retention on 324 10th grade high school ELA and Math MCAS scores in Massachusetts as a whole while also including economically disadvantaged and English as a Second Language selected-student populations. The results indicate that teacher retention specifically does not have much of an effect on their scores. The effects appeared to be slightly greater with the ELA MCAS scores in schools that are low-performing where the retention rates are lower due to the economically disadvantaged populations.