Metropolitan-rural voting patterns in U.S. legislative elections

This paper examines the relationship between partisan political success, in both the United States House of Representatives and in the lower houses of U.S. state legislatures, and distance from the central city. The increasing Republican success over time, first in suburbs generally, and then in outer suburbs, is illustrated. Correspondingly, the paper shows that Democrats have retained their advantage in the central city, lost advantage in the rural areas and compete most effectively in inner ring suburbs. Also, different measures of distance from the central city (distance in miles, in types of living arrangements (e.g. urban, suburban, and rural) and in terms of how much of the district is urban) are considered, measured and then incorporated into causal models. The models show that both the number of miles from the central city and the types of living arrangements has a statistically significant impact on party electoral fortunes. The models also show the impact of region and of several demographic factors, including race, income and the percentage of citizens in the district who receive social services

From the suburbs to the house: The metropolitan–rural population and the success of women candidates

We analyze the voting behavior of metropolitan and rural residents in relation to women’s legislative representation. Examining election data on the U.S. House and all lower state houses, we find that the greater the metropolitan population in a legislative district, the more likely it is to be represented by a woman. We extrapolate from these findings that the modern increase in women’s representation can be attributed in part to the rural-to-suburban shift in population and legislative seats

Legislative/judicial interaction: do court ideologies constrain legislative action?

This paper seeks to contribute to our understanding of the degree of success enjoyed by bills in state legislatures. More specifically, we propose a model of bill success that includes a measure of judicial preferences such that we can ascertain the extent to which judicial ideology and perceived judicial climate constrain legislative behavior. We argue that liberal bills are less likely to be enacted in states where the court of last resort is also liberal as opponents will be concerned that the high court will read the legislation too expansively. There is, thus, additional incentive to mobilize to prevent passage of liberal bills in these states

Assessing Criterion Validity of Using Internet Searches as a Measure of Public Attention

We examine the criterion validity of using internet searches as a measure of public attention to United States Supreme Court (USSC) cases. First, we construct a measure of public attention to three cases by comparing relevant search terms in Google Trends to one top search terms of the year, then sum the measure week by week during the period of the research design. To test the measure’s criterion validity, we replicate Scott and Saunders’ (2006) models using their dataset (created by conducting phone interviews of a national sample using random digit dialing) that was designed to assess awareness of USSC decisions. We fnd that public attention as measured by Google Trends data is predictive of public awareness of USSC decisions for two of their three models. We conclude that using free, publicly available big data to measure public attention to USSC cases has criterion validity, and is a valuable tool for researchers studying public policy and process. Our fndings contribute to the body of research by demonstrating the validity of internet searches as a measure of public attention beyond its validity in elections and public policy, as Swearingen and Ripberger (2014) and Ripberger (2011) have done

Here’s Looking at You: Public- Versus Elite-Driven Models of Presidential Primary Elections

Objective. This study advances the presidential primary literature in two ways. First, since many studies in this literature advocate for more detailed theoretical development, we incorporate an interdisciplinary approach by utilizing social contagion theory from the field of sociology. Second, presidential primaries do not adequately explore what role the public plays during the invisible primary. We thus incorporate Google Trends data into presidential primary models to account for the relative amount of public attention for each presidential primary candidate. Methods. We use fixed effects regression to determine the impact of public attention on a candidate’s share of the contested primary vote (CPV). Results. We find that increased public attention leads to higher levels of support for a candidate in the Iowa caucuses, New Hampshire primary, and CPV. Conclusion. These findings illustrate the extra-voting role the public plays in presidential primary elections and helps us further distinguish how party elites, voters, and candidates uniquely determine the selection of our executive

Life of the Party: Social Networks, Public Attention, and the Importance of Shocks in the Presidential Nomination Process

We examine the effects of shocks on the invisible Presidential primary in the United States. First, we build on existing models using an algorithm simulating social network shocks. Findings show that positive shocks significantly aid the lead candidate’s chances of winning in the invisible primary. Negative shocks, however, are less detrimental to a lead candidate than positive shocks are helpful, as the leader is often able to survive a negative shock and still emerge victorious. Broad empirical tests demonstrate the importance of shocks as well. Beyond the importance of shocks, findings also suggest that Presidential candidate success in the invisible primary owes more to public- than elite-driven factors

Catch Me if You Can: Using a Threshold Model to Simulate Support for Presidential Candidates in the Invisible Primary

The invisible primary is an important time in United States Presidential primary politics as candidates gain momentum for their campaigns before they compete formally in the first state caucus (Iowa) and primaries (e.g. New Hampshire). This critical period has not been possible to observe, hence the name. However, by simulating networks of primary followers, we can explicate hypotheses for how messages travel through networks to affect voter preferences. To do so, we use a threshold model to drive our simulated network analysis testing spread of public support for candidates in invisible primaries. We assign voter thresholds for candidates and vary number of voters, attachment to candidates and decay. We also vary social graph structure and model. Results of the algorithm show effects of size of lead, an unwavering base of support, and information loss

Motor Performance and Quality of Life in a Community Exercise Program for Parkinson Disease

We investigated the effect of a comprehensive community program composed of exercise, mindfulness practice, and education on motor function and quality of life in individuals with Parkinson disease (PD). Thirty-six participants completed physical and quality-of-life assessments independently at baseline and 12 months. Physical assessments showed stability or improvement in functional mobility, integrated strength, and walking ability over the 1-year interval. PDQ-39 measures showed improvement in 6 of 8 indices: mobility, activities of daily living, emotional well-being, stigma reduction, social support, and bodily discomfort. Our results demonstrate the effectiveness of exercise, mindfulness, and education in community and group settings

Evaluating the diversity and utility of materials proposed by generative models

Generative machine learning models can use data generated by scientific modeling to create large quantities of novel material structures. Here, we assess how one state-of-the-art generative model, the physics-guided crystal generation model (PGCGM), can be used as part of the inverse design process. We show that the default PGCGM's input space is not smooth with respect to parameter variation, making material optimization difficult and limited. We also demonstrate that most generated structures are predicted to be thermodynamically unstable by a separate property-prediction model, partially due to out-of-domain data challenges. Our findings suggest how generative models might be improved to enable better inverse design.Comment: 12 pages, 9 figures. Published at SynS & ML @ ICML2023: https://openreview.net/forum?id=2ZYbmYTKo

The Grizzly, September 26, 1980

Equipment Stolen From New Ritter Center • Conversion Eases Skyrocketing Utility Costs • Dean\u27s Office Discloses Frat GPAs • New Windows for NMD • New Spanish Lecturers Interviewed • IF, USGA to Sponsor Fall Picnic • Anderson Addresses College Crowd At Phila. Rally • College Invaded By World Of Technology • Draft Registration Closely Examined • Campus Grounds Receive Face Lift • Hamilton Presents Astronomy Discoveries • Try-outs for Trial by Jury • Weekends at Ursinus • Harriers Place 2nd At Lafayette Invitational • Grizzly Football Handled By W. Maryland • Sports Profile: Craig Walck • Field Hockey Finishes Week Undefeated • Strong Hitting By Bear V-Ball Outdoes Moravian • Offense Sputters As Bears Lose • Kreiger Powers Heathenshttps://digitalcommons.ursinus.edu/grizzlynews/1041/thumbnail.jp