Other Legal Studies Commons^™

Why Domestic Violence Offenders Don't Give Up Their Guns, Natalie Nanasi

Faculty Journal Articles and Book Chapters

Perpetrators of intimate partner violence are barred by federal law and many states' laws from possessing firearms. While such prohibitions enjoy popular support, they are sporadically and inconsistently enforced, placing the lives of survivors at risk when offenders do not voluntarily comply. Many experts, including this author, have offered legal and policy solutions to increase the likelihood that perpetrators of domestic violence will either willingly relinquish their guns or otherwise be dispossessed of them. But these proposals may have been premature.

This Article is the first to take a step back and inquire why offenders do not surrender their firearms …

Examining Bias In Jury Selection For Criminal Trials In Dallas County, Megan Ball, Brandon Birmingham, Matt Farrow, Katherine Mitchell, Bivin Sadler, Lynne Stokes

SMU Data Science Review

One of the hallmarks of the American judicial system is the concept of trial by jury, and for said trial to consist of an impartial jury of your peers. Several landmark legal cases in the history of the United States have challenged this notion of equal representation by jury—most notably Batson v. Kentucky, 476 U.S. 79 (1986). Most of the previous research, focus, and legal precedence has centered around peremptory challenges and attempting to prove if bias was suspected in excluding certain jurors from serving. Few studies, however, focus on examining challenges for cause based on self-reported biases from the …

Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels

SMU Data Science Review

In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …