In my experience, every kind of writing requires some kind of self-soothing Jedi mind trick, and, when it comes to essay composition, the rectangle is mine. What had seemed an impossible task transformed into a practical matter of six little arrows. The first essay anybody writes is for school. But the only examples I remember are the ones I wrote at the end, in my A-level exams. One compared Hitler to Stalin. I was proudest of the essay that considered whether the poet John Milton--pace William Blake--was "of the devil's party without knowing it." I did well on those standardized tests, but even passing was far from a foregone conclusion. I'd screwed up my mocks, the year before, smoking too much weed and studying rarely. Since then, I'd cleaned up my act--a bit--but was still overwhelmed by the task before me. My rested on a few essays written in the school hall under a three-hour time constraint?

Migration has been a core topic in German political debate, from millions of expellees post World War II over labor migration to refugee movements in the recent past. Studying political speech regarding such wide-ranging phenomena in depth traditionally required extensive manual annotations, limiting the scope of analysis to small subsets of the data. Large language models (LLMs) have the potential to partially automate even complex annotation tasks. We provide an extensive evaluation of a multiple LLMs in annotating (anti-)solidarity subtypes in German parliamentary debates compared to a large set of thousands of human reference annotations (gathered over a year). We evaluate the influence of model size, prompting differences, fine-tuning, historical versus contemporary data; and we investigate systematic errors. Beyond methodological evaluation, we also interpret the resulting annotations from a social science lense, gaining deeper insight into (anti-)solidarity trends towards migrants in the German post-World War II period and recent past. Our data reveals a high degree of migrant-directed solidarity in the postwar period, as well as a strong trend towards anti-solidarity in the German parliament since 2015, motivating further research. These findings highlight the promise of LLMs for political text analysis and the importance of migration debates in Germany, where demographic decline and labor shortages coexist with rising polarization.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Decades ago, when I was a new teacher, my colleagues and I used to joke about the constant barrage of changes. "Public education is a moving target you can never hit," a mentor teacher once told me. Huge shifts are coming again. "I'm afraid I'm going to be put in prison for being trans," one of my students said to me after class last week.

The United States military has struck a number of cities in Yemen, including the capital, Sanaa, and the key port city of Hodeidah. Forces from the US Central Command (CENTCOM), the military command responsible for US forces in the Middle East, "conducted strikes on 15 Houthi targets in Iranian-backed Houthi-controlled areas of Yemen today", it said on X on Friday. Four strikes targeted Sanaa and seven hit Hodeidah, according to the Houthi-run Al Masirah TV network. Correspondents with the AFP news agency also reported hearing loud explosions in both cities. The Hodeidah strikes hit the airport and the Katheib area, which has a Houthi-controlled military base, Al Masirah said.

When a book titled Terrorism: A Very Short Introduction, written by the British professor and historian Charles Townshend, was found by police near the pro-Palestine student encampment at Columbia University, it was held up by New York Police Department (NYPD) Deputy Commissioner Kaz Daughtry as evidence of some kind of foreign, radicalising influence on student activism. Apparently, for Daughtry, reading a book on terrorism is evidence of radicalisation. Knowing about terrorism makes you at risk of committing terrorism. Finding a book near a student encampment confirms that pro-Palestine solidarity is linked to terrorism. What Daughtry was arguably trying to do was darken Palestine activism on college campuses across the United States with the association of terrorism.

Yemen's Houthi rebels say they have targeted what they claim to be an Israeli cargo ship, the MSC Silver, in the Gulf of Aden near the entrance to the Red Sea with a number of missiles. Houthi military spokesperson Yahya Sarea did not elaborate, but in a statement on Tuesday said the group had also used drones to target a number of United States warships in the Red Sea and Arabian Sea as well as sites in the southern Israeli resort town of Eilat. However, the British maritime security firm Ambrey said the container ship targeted by the Houthis on Tuesday was Liberia-flagged and headed for Somalia. The operator was publicly listed as [in] cooperation with ZIM and regularly called [at] Israeli ports," the Ambrey advisory note said. Zim Integrated Shipping Services Ltd, commonly known as ZIM, is a publicly held Israeli international cargo shipping company based in Israel.

Israeli security forces restricted young Palestinians from entering the Al-Aqsa Mosque in Jerusalem for prayers on Friday and deployed in strength across the Old City and beyond to quell any unrest spilling over from the conflict in Gaza. In the occupied West Bank, Israeli troops killed four Palestinians during raids, the official Palestinian news agency WAFA said. Two of the dead were identified by fighter groups as their members. Large numbers of Israeli police kept guard around Al-Aqsa, a flashpoint and often the scene of clashes, as Palestinians gathered for Friday prayers, reports said. At one point, the police fired tear gas at the Palestinians, according to Reuters.

The research explores the steerability of Large Language Models (LLMs), particularly OpenAI's ChatGPT iterations. By employing a behavioral psychology framework called OCEAN (Openness, Conscientiousness, Extroversion, Agreeableness, Neuroticism), we quantitatively gauged the model's responsiveness to tailored prompts. When asked to generate text mimicking an extroverted personality, OCEAN scored the language alignment to that behavioral trait. In our analysis, while "openness" presented linguistic ambiguity, "conscientiousness" and "neuroticism" were distinctly evoked in the OCEAN framework, with "extroversion" and "agreeableness" showcasing a notable overlap yet distinct separation from other traits. Our findings underscore GPT's versatility and ability to discern and adapt to nuanced instructions. Furthermore, historical figure simulations highlighted the LLM's capacity to internalize and project instructible personas, precisely replicating their philosophies and dialogic styles. However, the rapid advancements in LLM capabilities and the opaque nature of some training techniques make metric proposals degrade rapidly. Our research emphasizes a quantitative role to describe steerability in LLMs, presenting both its promise and areas for further refinement in aligning its progress to human intentions.

This paper proposes a methodology for exploring how linguistic behaviour on social media can be used to explore societal reactions to important events such as those that transpired during the SARS CoV2 pandemic. In particular, where spatial and temporal aspects of events are important features. Our methodology consists of grounding spatial-temporal categories in tweet usage trends using time-series analysis and clustering. Salient terms in each category were then identified through qualitative comparative analysis based on scaled f-scores aggregated into hand-coded categories. To exemplify this approach, we conducted a case study on the first wave of the coronavirus in Italy. We used our proposed methodology to explore existing psychological observations which claimed that physical distance from events affects what is communicated about them. We confirmed these findings by showing that the epicentre of the disease and peripheral regions correspond to clear time-series clusters and that those living in the epicentre of the SARS CoV2 outbreak were more focused on solidarity and policy than those from more peripheral regions. Furthermore, we also found that temporal categories corresponded closely to policy changes during the handling of the pandemic.

We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and interdisciplinary insurance literature. Tracing the interaction of uncertainty, fairness and responsibility in insurance provides a fresh perspective on fairness in machine learning. We link insurance fairness conceptions to their machine learning relatives, and use this bridge to problematize fairness as calibration. In this process, we bring to the forefront three themes that have been largely overlooked in the machine learning literature: responsibility, performativity and tensions between aggregate and individual.