Kansas City, Missouri, has seen record rent growth in recent years. This comes at a time when the city reels from a deep shortage in affordable housing, an ongoing eviction crisis, and persistent racial segregation. How is investment in new rental housing intertwined with entrenched patterns of anti-Black residential segregation and long-standing spatial inequalities? How are speculative real estate practices understood and legitimated as neutral, rational, and nonracial? This article examines these questions through ethnographic fieldwork on the everyday practices of real estate development. By examining the calculative routines and anticipatory devices deployed in the financing and designing of rental housing, the article shows how the constitution of market-rate apartment buildings into legible, predictable, and tradable financial assets is premised on reinscribing anti-Blackness onto the built environment vis-& agrave;-vis ostensibly neutral market categories and financial concepts.
Prehospital emergency medicine is a high-risk environment where time-critical decisions must be made under adverse conditions. Artificial Intelligence (AI) holds the potential to enhance patient care through data-driven decision support. This narrative review analyses current and emerging AI applications within emergency medical services and evaluates their impact on the quality dimensions defined by Donabedian (structural, process, and outcome quality). It is intended for paramedics and clinicians in the prehospital setting and aims to encourage further engagement with AI in emergency medical services to reduce cognitive load and ultimately improve patient outcomes. A review of international literature was conducted to identify AI applications in emergency medical services. The analysis was structured according to typical operational phases: emergency call interrogation, dispatch and resource allocation, operational-tactical applications, and clinical applications. Additionally, hypothetical scenarios and illustrative case studies were developed to demonstrate operational pathways and practical utility. Evaluation was guided by established healthcare quality indicators and conducted within a narrative, qualitatively integrative framework, reflecting the methodological heterogeneity of the available evidence. AI-based technologies demonstrate promising applications across all operational phases, but implementation is still mostly limited to pilot projects and local solutions. In emergency call interrogation, AI-driven speech recognition can increase the detection rate of out-of-hospital cardiac arrest by 43
Programmers are turning to AI coding assistants to answer questions about their code. Benchmarks are needed to soundly evaluate these systems and understand their performance. To enable such a study, we curate a benchmark of real-world contextualized questions derived from Github pull request comments. Out of this work, we present RubberDuckBench: a multilingual benchmark of questions about code, along with detailed rubrics for evaluating answers. We evaluate a diverse set of 20 LLMs (proprietary open-source) on answering these questions. We find that even state of the art models fail to give consistent, correct responses across the benchmark. Grok 4 (69.29
A growing number of introductory physics instructors are implementing active learning methods in their classrooms, and they are modifying these methods to fit their local instructional contexts. However, we lack a detailed framework for describing the range of what these instructor adaptations of active learning methods look like in practice. Existing studies apply structured protocols to classroom observations and report descriptive statistics (e.g., the fraction of class time spent on each activity), but this approach overlooks the complex nature of instruction. In this study, we apply network analysis techniques to classroom observations to define a typology of active learning that considers the temporal and interactional nature of instructional practices. We analyze video data from 30 instructors at 27 institutions who implemented one of the following named active learning methods in their introductory physics or astronomy courses: Investigative Science Learning Environment (ISLE), Peer Instruction, Tutorials, and Student-Centered Active Learning Environment with Upside-down Pedagogies (SCALE-UP). We first create one classroom observation network per instructor that captures temporal sequences of activities measured using the Classroom Observation Protocol for Undergraduate STEM (COPUS). We then calculate the cosine similarity between all pairs of observation networks, create a similarity network where each instructor’s classroom observation network is a node and the edges represent cosine similarity, and apply the Infomap clustering algorithm to the similarity network to identify types of active learning instruction. We find five types of instruction: clicker lecture, dialogic clicker lecture, dialogic lecture with short groupwork activities, short groupwork activities, and long groupwork activities. We find no significant relationship between these instruction types and the named active learning methods; instead, implementations of each of the four methods are spread across different instruction types. This result prompts a shift in the way we discuss and study active learning: the names of developed active learning methods may not actually reflect classroom instruction. We also find that student conceptual learning does not vary across the identified instruction types, suggesting that instructors may be flexible in the style in which they implement these methods without sacrificing effectiveness. We discuss the implications of these results for both research and the professional development of college physics instructors.
This conceptual paper introduces the Transformation-Affirmation-Recognition (TAR) model, a framework that reconceptualizes transformation as a psychosocial process grounded in the dynamic interplay between self-affirmation and social recognition. Drawing on Mezirow's theory of transformative learning, Steele's self-affirmation theory, and Honneth's recognition theory, the paper argues that transformation is sustained not by cognition alone but by the affective and relational forces that enable individuals to remain in contact with difficult emotions such as discomfort. Self-affirmation preserves a sense of integrity when one's meaning structures are disrupted, while recognition validates the emerging self within a social context. Together, these forces form a dialectical movement that allows learners to engage discomfort as a developmental resource rather than as a threat. An illustrative reflection demonstrates how the TAR process operates in practice, revealing how affirmation and recognition jointly enable resilience and growth.
