Jurisprudence Brett Kavanaugh Is Trying to Walk Back "Kavanaugh Stops." Justice Brett Kavanaugh does not seem happy that his name has become synonymous with racist immigration enforcement. In September, the justice wrote that Hispanic residents' "apparent ethnicity" could be a "relevant factor" in federal agents' decision to stop them and demand proof of citizenship. Immigration and Customs Enforcement and Customs and Border Protection promptly seized upon his opinion as a license to stop any Hispanic person on the basis of race--often with excessive, even sadistic force --and detain them until they proved their lawful presence. Law professor Anil Kalhan termed these encounters "Kavanaugh stops," and the name swiftly caught on as evidence mounted that they had become standard practice across the country.

Recent advancements in large language models (LLMs) have opened new avenues for enhancing text classification efficiency in political science, surpassing traditional machine learning methods that often require extensive feature engineering, human labeling, and task-specific training. However, their effectiveness in achieving high classification accuracy remains questionable. This paper introduces a three-stage in-context learning approach that leverages LLMs to improve classification accuracy while minimizing experimental costs. Our method incorporates automatic enhanced prompt generation, adaptive exemplar selection, and a consensus mechanism that resolves discrepancies between two weaker LLMs, refined by an advanced LLM. We validate our approach using datasets from the BBC news reports, Kavanaugh Supreme Court confirmation, and 2018 election campaign ads. The results show significant improvements in classification F1 score (+0.36 for zero-shot classification) with manageable economic costs (-78% compared with human labeling), demonstrating that our method effectively addresses the limitations of traditional machine learning while offering a scalable and reliable solution for text analysis in political science.

Recent efforts to address hallucinations in Large Language Models (LLMs) have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections. Yet, these citations often point to entire documents or paragraphs, burdening users with extensive verification work. In this paper, we introduce a locally-attributable text generation approach, prioritizing concise attributions. Our method, named "Attribute First, then Generate", breaks down the conventional end-to-end generation process into three intuitive steps: content selection, sentence planning, and sequential sentence generation. By initially identifying relevant source segments ("select first") and then conditioning the generation process on them ("then generate"), we ensure these segments also act as the output's fine-grained attributions ("select" becomes "attribute"). Tested on Multi-document Summarization and Long-form Question-answering, our method not only yields more concise citations than the baselines but also maintains - and in some cases enhances - both generation quality and attribution accuracy. Furthermore, it significantly reduces the time required for fact verification by human assessors.

Fox News correspondent David Spunt has the latest on Congress' response to the failed assassination attempt of Justice Brett Kavanaugh on'Special Report.' Washington Post deputy editorial editor Ruth Marcus wants to make sure people are aware "deranged individuals do deranged things" on "both ends of the political spectrum" before assigning blame for the man who was arrested near the Maryland home of Supreme Court Justice Brett Kavanaugh. On Wednesday, an armed California man identified as Nicholas John Roske was carrying a gun, knife and pepper spray when arrested outside Kavanaugh's home. He told officers that he wanted "to give his life purpose" and purchased the gun and other items for the purpose of breaking into Kavanaugh's home and killing the justice and then himself. A piece published Thursday night by Marcus headlined, "The Kavanaugh threat exposed weaknesses in judicial security -- and our discourse," admitted the incident "could have ended in unfathomable tragedy" but urged readers not to assign blame or dismiss people who created the environment that "fueled" the assassination attempt.

In an ideal world, the Supreme Court would provide stability in the run-up to a presidential election, imposing uniform rules based on long-accepted principles of election law. We do not live in that world. One week out from the 2020 election, four Supreme Court justices have launched a scorched-earth mission against voting rights. They teed up a Bush v. Gore reprise that could hand Donald Trump an unearned victory. These justices are in open revolt against voting rights, abandoning the pretense of "voter fraud" and embracing state legislatures' right to disenfranchise their constituents.

Former Democrat congressional aide Jackson Cosko is sentenced for posting the private information of five Republican senators on Wikipedia during the Kavanaugh Supreme Court confirmation hearings; Griff Jenkins reports. A second aide to Sen. Maggie Hassan, D-N.H., has been sentenced in a scheme to break into Hassan's office to obtain and publicly post the personal information of several Republican politicians amid contentious confirmation hearings for Justice Brett Kavanaugh. The 24-year-old former aide, Samantha Deforest Davis, was sentenced to two years of supervised probation with 200 hours of community service, with a suspended sentence of 180 days in prison. She was ordered to "stay away from [Hassan's] office to include current and former staff, and to not use Tor or anonymized computer applications," the Justice Department said in a statement. Davis was a staff assistant in Hassan's office from August 2017 until last December.

This Employment Law This Week Monthly Rundown features a recap of the biggest employment law trends from 2018 and a look ahead at what's to come in 2019. Specifically, this episode includes the following: 1. #MeToo Movement in the Workplace For employers, 2018 was the year of #MeToo. While the movement began in the fall of 2017, last year, it touched every aspect of employment law--from harassment training to arbitration. Jennifer Gefsky (Member of the Firm, Epstein Becker Green): "I think if the #MeToo movement taught us one thing, it's that employers face significant liability and risk in the event that allegations are made against any employee or supervisor or the highest-level executive at the company." In 2019, we can expect to see more legislative action, particularly in the area of equal pay, where much of the #MeToo focus has shifted.

Shares finished down 7.6% at $134.05, a decline that sliced 75 points off the Dow Jones Industrial Average and sent the stock to its lowest close in more than two and a half years. Shares are down more than 15% from a year ago. IBM on Tuesday reported adjusted profit that topped Wall Street's forecast. Its 2.1% slide in revenue from a year ago served as a reminder of the company's yearslong struggle to ditch its legacy image as a computer maker and refocus on fast-growing businesses such as cloud computing and services driven by artificial intelligence. "It was clearly a disappointing quarter," said John Conti, a partner at SeaBridge Investment Advisors LLC, which is an investor in IBM.

Sharing a name with a famous person can prompt endless jokes and comments -- but in these particularly politically-charged times, having the same name as a political figure can be especially tiresome. That's something a young man from Kentucky named Brett Kavanagh has learned only too well in recent weeks: On Friday, Brett, 27, complained about the recent woes of having his name, prompting others with famous names to commiserate. Women named Siri and Alexa, and men named Michael Jackson and Bruce Lee, all tweeted about how hard it is to have a well-known name. His tweet inspired others to chime in, including this person who pointed to a Scottish man named Steve Bannon -- who is not the same as Breitbart's Steve Bannon A man named Bruce Y. Lee knows the struggle This Brett, who works in customer service and lives in Louisville, spells his last name differently from new Supreme Court Justice Brett Kavanaugh, but it seems their nearly-identical names has caused him some trouble. Tough times: Brett (pictured) doesn't spell his name the same way as the judge, either'This is a terrible time to be named Brett Kavanagh,' he tweeted.

Republican Arizona Sen. Jeff Flake has developed a reputation as someone who is willing to criticize Donald Trump and his party enablers in unusually blunt terms--but not generally willing to use his status as a potential swing vote in a narrowly divided Senate to initiate investigations into administration corruption, protect Robert Mueller's special counsel investigation, or achieve any of the other good-governance goals he might be expected to support given his feelings on Trumpism. That was Flake's reputation, at least, before he announced a dramatic effort last Friday to delay Brett Kavanaugh's confirmation vote until the FBI could conduct a weeklong review of sexual assault accusations against the Supreme Court nominee. When it became clear that fellow Trump-skeptic GOP senators Susan Collins and Lisa Murkowski supported Flake's position, the White House almost immediately agreed to order just such an investigation. Over the weekend, though, there was some grinding in the gears. NBC reported, and other outlets confirmed, that the White House had set narrow limits on which individuals the FBI was allowed to contact.