Bibliographic record
Abstract
This dissertation is composed of three unrelated chapters, all of which are on different topics. Chapter 1 uses the Behavioral Risk Factor Surveillance System (BRFSS) survey data from 2013 to 2018 to investigate diabetes education's effects among diabetes respondents on different health outcomes and risky behaviors. I utilize Propensity Coarsened Exact Matching (CEM) method for diabetes education and find that receiving diabetes education positively affects one's self-reported health outcomes and negatively affects one's propensity to engage in risky behaviors. Specifically, I show evidence that receiving diabetes education reduces the number of days that the survey participants do not feel well, physically. It also reduces respondent's alcohol intake and the probability of respondents being a current smoker. Moreover, I also show that having diabetes education increases the frequency of having an A1C check-up and increases physical activity among respondents. Chapter 2, co-authored with Chandler Clemons, investigates how economic uncertainty, specifically stock market uncertainty, correlates to individuals' life satisfaction. Using expected price volatility (VIX) as our anticipatory indicator and life satisfaction as our measure of utility, our hypothesis is built on the Anticipatory Utility framework, which suggests that people also derive utility from their beliefs. After accounting for associations with the unemployment rate and stock ownership, we find a positive relationship between the VIX and low self-reported life satisfaction. This analysis captures the contemporaneous effects of future beliefs and indicates that the future's economic sentiment plays an important role in individuals' feelings about the present. Chapter 3 is a pilot study that I co-author with my academic advisor Joshua Tasoff, Professor Emiliano Huet-Vaughn from Pomona College, and Professor Eva Vivalt from the University of Toronto. We are motivated by a norm that when faced with the treatment of animals in factory farms, many individuals reconsider the ethics of their omnivorous diet, but people may not want to be confronted with information that implicates their lifestyle as a cause of large-scale suffering. We present a laboratory experiment designed to test for such information avoidance. Using a formal model of cognitive dissonance, we will price people's value for maintaining consonant beliefs. Specifically, we hypothesize that information avoiders are individuals who are, on average, more influenced by ethical messaging. Individuals who have a high cost to hold dissonant beliefs will, upon being informed, either feel guilt eating meat or feel a painful obligation to change their diet. It is why we believe they avoid information in the first place. We will also test whether people conform to a model of deontological moral rules, in which there is a discrete psychic cost to eating meat or whether they more closely behave according to a utilitarian model of morality in which the more meat they eat, the greater the psychic cost.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".