MATTHEW KENDALL
MATTHEW KENDALL
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About Me
About Me
I am a PhD Candidate at Claremont Graduate University, focusing my research on using quantitative economic tools to analyze public policy. Through my experience working with electoral campaigns, the criminal justice system, and police agencies, my goal is to use causal inference techniques to give concrete recommendations to policymakers and public officials. Alongside my research, I have a passion for teaching and aim to share my expertise using informed pedagogy.
SELECTED WORK
SELECTED WORK
Judicial Diversion: Should Judges Be Giving Second Chances? (Working Paper)
A diversion is one of many programs which, if completed, allow a defendant in a low-level to have their charges dismissed. The goal of these diversions is to benefit the defendant, lessen their chances to recidivate, and lessen the burden on judicial and prosecutorial resources. Diversions in California prior to 2020 were prosecutor initiated, and accepted by the entire court before going into effect. AB 3234 allowed judges to solely initiate a judicial diversion for most misdemeanor offenses. This paper aims to answer what effect this broadening of judicial power has on the effectiveness of diversion programs, including recidivism and the costs associated with it compared to the benefits of largely avoiding a criminal court.
Prosecutor vs. AI: Is Minority Report a Good Idea? (Working Paper)
The idea of a perfect predictive model for criminal behavior is the philosopher's stone of criminology, and many practitioners are rightfully cautious of implementing anything claiming to be one. Part of the difficulty in creating a true predictive model is the many different and often conflicting goals of a criminal justice system; deterrence, prevention, restoration, and reintegration are often directly opposed to one another. By training a machine learning algorithm to identify edge cases that teeter between full reformation and frequent reoccurring reoffences, we can correctly apply the sentencing that maximize these polarizing criminological goals. Using this model, we can then test the outcomes of prosecutors who acted as if they followed the algorithms recommendations against those who did not. We can then see how sensitive this machine learning algorithm's accuracy is to changing which statistic to maximize.
EDUCATION
EDUCATION
Claremont Graduate University | Ph.D Economics (Candidate)
Claremont Graduate University| Master of Economics May 2023
University of Arizona | Bachelor of Politics, Philosophy, Economics, and Law May 2018
TEACHING EXPERIENCE
TEACHING EXPERIENCE
Claremont Graduate University | Teaching Assistant of ECON 317 - Game Theory Spring 2024
Frontline Educator | Substitute Teacher - All Grades Fall 2020 - Summer 2021
Basis Ed | English Teacher - 1st Grade Fall 2020
Etonkids | Montessori ESL Specialist - Kindergarten Fall 2018 - Spring 2020
TEACHING PHILOSOPHY
TEACHING PHILOSOPHY
The best teacher is someone who can take the most complex and complicated topics and explain them to a five year old. It just so happens that I am very good at explaining things to five year olds.
We are living in a time where there are Ivy League lectures posted in their entirety on YouTube, and at least a quarter of essays submitted begin with "While an AI cannot give opinions...", it is a teacher's job to give an experience beyond what student already has access to. Learning does not happen from an uninteractable video, and information synthesis does not happen when a question can be easily answered with a chatbot. A lecturer's job is to dispense important information. It is a teacher's job to turn a lecture into a classroom. To truly learn, a student must engage with the material in real time, and collaborate with each other in order to create something new. No lecture should go ten minutes without this process, and no assignment should be able to meet standards using only what is available on the slides.
Failure is also a part of the learning process, despite how scary the concept can be. Whether as small as reading a passage a second time, or as large and needing to scrap an entire project and start from scratch, these are the moments that are not only inevitable in a learning process but also those that lead to true mastery. Failure should never be something an educator makes their students fear, and as such they should not punish their students for it. All assignments and even many tests should be able to be redone for full credit. After all, why would a student read the feedback on an assignment they will never look at again, or a test whose grade they cannot change? Often, they do not. This philosophy towards grades also allows a teacher to be much more constructive with their feedback - feeling free to give out failing grades knowing that every student can achieve the grade they want by following the learning process.
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