Law enforcement reporting following sexual assault is lower than for other violent crimes. Sexual assault nurse examiners (SANEs) provide care for patients in the acute period following sexual assault and are well-positioned to identify and address barriers to reporting. We examined data from medical forensic examination records documented by SANEs for a 5-year period (2011–2015). We examined 347 records of women 18 and older to identify factors associated with law enforcement reporting at the time of the exam using binomial logistic regression to construct odds ratios (OR). A total of 56.5% of patients in the sample reported to law enforcement. Patients who did not voluntarily consume alcohol were more likely to report than those who did (OR = 4.45; p = .001). Patients who were not students were more likely to report than students (OR = 3.24; p = .002). Patients who had a medical forensic exam within 32 hr of the assault were more likely to report than those having exams after 32 hr (OR = 2.68; p = .007). Patients who had anogenital and/or bodily injuries were more likely to report than those who had no injuries (OR = 2.50; p = .008). Patients who were penetrated (vaginally, orally, and/or anally) were more likely to report than those who were not penetrated (OR = 2.50; p = .056). Knowing the assailant, having multiple assailants, and patient and assailant race/ethnicity were not associated with different likelihood of reporting to law enforcement. SANEs and others who work with victims of sexual assault can use data to understand and address barriers to reporting.

ChatGPT, a language-learning model chatbot, has garnered considerable attention for its ability to respond to users’ questions. Using data from 14 countries and 186 institutions, we compare ChatGPT and student performance for 28,085 questions from accounting assessments and textbook test banks. As of January 2023, ChatGPT provides correct answers for 56.5 percent of questions and partially correct answers for an additional 9.4 percent of questions. When considering point values for questions, students significantly outperform ChatGPT with a 76.7 percent average on assessments compared to 47.5 percent for ChatGPT if no partial credit is awarded and 56.5 percent if partial credit is awarded. Still, ChatGPT performs better than the student average for 15.8 percent of assessments when we include partial credit. We provide evidence of how ChatGPT performs on different question types, accounting topics, class levels, open/closed assessments, and test bank questions. We also discuss implications for accounting education and research.

Our research empirically examines the relationship between tax avoidance and the likelihood of incurring a tax-related restatement, as well as the effects tax restatements have on future tax avoidance behavior. We predict and find that the association between tax avoidance and the likelihood of a tax-related restatement is nonlinear. Specifically, both relatively high levels and relatively low levels of tax avoidance compared to peer firms increase the likelihood of incurring a tax-related restatement. We consider whether the increased likelihood for high avoiders is attributable to obfuscation necessary to escape detection of tax avoidance or weak corporate governance. For high avoiders, we find evidence that both obfuscation and weak corporate governance may contribute to the likelihood of a tax-related restatement. As low avoiders should not need to obfuscate, we focus on corporate governance and find an increased likelihood when governance is weak. In response to a tax-related restatement announcement, we document a decline in tax avoidance for firms that have relatively high tax avoidance prior to the restatement announcement. We attribute this to the increased level of Internal Revenue Service (IRS) monitoring and strengthening of corporate governance that we observe post-restatement announcement. In contrast, we do not find evidence that low avoidance firms alter their tax avoidance after a tax-related restatement announcement, consistent with our finding that corporate governance does not improve post-restatement for low avoiders.

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To protect the privacy and other civil liberties of citizens, federal courts place limits on the power and actions of government. These limits create a need for balance between the IRS' mission of tax law enforcement and taxpayers' privacy rights. A much-watched contemporary lower court case intersecting cryptocurrencies, summons power, and taxpayer privacy is Coinbase v. U.S. There, the IRS sought to summons massive amounts of customer information from Coinbase, a cryptocurrency exchange platform. This article examines the history of the IRS summons power and argues that the Coinbase court correctly extended a wealth of summons enforcement case law by weighing the protection of taxpayer privacy with the tax compliance mission of the IRS. By allowing the IRS summons to stand, but limiting and defining the scope of relevant records allowed to be examined, the Coinbase court correctly balanced IRS tax enforcement with taxpayer data privacy.