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美国政治

The Politics of Criminalized Places: Aggressive Policing and Political Mobilization

·92 字·1 分钟
Polihot 政治行为 美国政治 Policing Political Mobilization Criminalization Political Behavior
JOP — 2026-09-14 Original The Politics of Criminalized Places: Aggressive Policing and Political Mobilization Daniel Naftel Abstract （原文未提供摘要） 中文 犯罪化地区的政治：激进警务与政治动员 Daniel Naftel 摘要 原文未提供摘要 关键词 激进警务、政治动员、犯罪化地区、政治行为、刑事司法 DOI: 10.1086/744063 查看原文 →

Durable majority gerrymanders: Where partisan gerrymandering can displace democracy

·586 字·2 分钟
Polihot 美国政治 研究方法 Gerrymandering Redistricting State Legislatures Democracy Electoral Geography
AJPS — 2026-08-10 Original Durable majority gerrymanders: Where partisan gerrymandering can displace democracy Maxwell Palmer, Benjamin Schneer Abstract Abstract We develop the concept of, and estimation tools for, durable majority gerrymanders : electoral district maps drawn to reduce an opposing political party's probability of winning a majority in a legislative chamber. Directly interpretable, forward‐looking, and motivated by the democratic principle that a party in power should have some chance of losing it, this measure provides new insights into the role of redistricting in state legislative elections. We show that, when map drawers are unconstrained, the ability to create durable majorities is so widespread that at least one party in every state can draw a map where a majority of legislative districts withstand almost any likely future electoral swing. Enacted maps are less durable due to a combination of underlying geography, voter partisanship, and state‐level guidelines. This paper provides the theoretical framework and empirical tools to understand which gerrymandered maps enable state‐level majorities to emerge and to endure.

Race-conscious admissions algorithms and the law

·801 字·2 分钟
Polihot 公共政策 美国政治 Affirmative Action Machine Learning College Admissions Algorithmic Fairness Supreme Court
PNAS 123/30 pp. e2509764123-e2509764123 2026-07-20 Original Race-conscious admissions algorithms and the law Alexandra Chouldechova, Daniel J. Hemel Abstract In recent years, colleges and universities have begun to use machine learning (ML) systems to inform admissions decisions. Meanwhile, in the 2023 case Students for Fair Admissions, Inc. v. President and Fellows of Harvard College , the Supreme Court held that colleges and universities may not make admissions decisions “on the basis of race.” These parallel developments—the rise of ML in admissions and the fall of race-based affirmative action—will force educational institutions, and ultimately courts, to confront the difficult question of what it means for ML systems to differentiate “on the basis of race.” We begin by mapping the Students for Fair Admissions decision onto different uses of race in predictive AI. We distinguish between “first-order” and “second-order” race consciousness at both the training and predictive phases of machine learning, and we argue that each category of race consciousness raises distinct legal and normative issues. We go on to show that the Students for Fair Admissions decision potentially permits—and even endorses—certain forms of race consciousness. Our analysis is grounded in the observation that the process of developing ML-based systems enables policymakers to calibrate decision making algorithms much more precisely and explicitly in response to specific criticisms of race-conscious affirmative action.