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1. Racial Economic Segregation across U.S. Public Schools, 1991–2022

   https://doi.org/10.1177/23328584241282274  

   Jang, Heewon (January 2024, AERA Open)

   Despite its substantive importance as the strongest predictor of racial achievement gaps, racial economic segregation has been understudied in the previous literature on segregation. This paper describes trends in racial economic segregation over the last three decades and decomposes these trends into different geographic scales (e.g., between-state, between-district, and within-district segregation). Racial economic segregation has decreased since the late 1990s, yet Black students are still considerably isolated in schools with higher poverty rates. Between-district segregation has been the largest component of racial economic segregation, whereas within-district segregation has grown steadily during the last three decades. Findings from this study suggest the imperativeness of interdistrict policy remedies while also highlighting intradistrict policy needs for promoting student diversity across schools in the district. 

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2. From residence to movement: The nature of racial segregation in everyday urban mobility

   https://doi.org/10.1177/0042098020978965  

   Candipan, Jennifer; Phillips, Nolan Edward; Sampson, Robert J; Small, Mario (February 2021, Urban Studies)

   null (Ed.)

   While research on racial segregation in cities has grown rapidly over the last several decades, its foundation remains the analysis of the neighbourhoods where people reside. However, contact between racial groups depends not merely on where people live, but also on where they travel over the course of everyday activities. To capture this reality, we propose a new measure of racial segregation – the segregated mobility index (SMI) – that captures the extent to which neighbourhoods of given racial compositions are connected to other types of neighbourhoods in equal measure. Based on hundreds of millions of geotagged tweets sent by over 375,000 Twitter users in the 50 largest US cities, we show that the SMI captures a distinct element of racial segregation, one that is related to, but not solely a function of, residential segregation. A city’s racial composition also matters; minority group threat, especially in cities with large Black populations and a troubled legacy of racial conflict, appears to depress movement across neighbourhoods in ways that produce previously undocumented forms of racial segregation. Our index, which could be constructed using other data sources, expands the possibilities for studying dynamic forms of racial segregation including their effects and shifts over time. 

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3. The Persistence of Segregation in the 21st Century

   Jargowsky, Paul A. (January 2018, Law & inequality)

   This article address whether, in the fifty years since the Kerner Commission and the Fair Housing Act, the US has made progress in reducing racial segregation. As a point of departure, I critique a recent report that claimed to document the “end of the segregated century.” I argue that while black-white segregation has declined, this report measured segregation in a way that overstated the extent of the changes. Further, I argue that looking at racial segregation in isolation ignores other demographic changes that have acted to magnify the impact of racial segregation. For example, economic segregation has been increasing in recent decades and interacts with racial segregation to produce high-poverty ghettos. Another change is the geographic restructuring of metropolitan areas, so that segregation is now increasingly organized by municipalities rather than by neighborhoods, with implications for access to publicly-funded amenities such as quality schooling. Finally, I show that there is a life-cycle aspect of segregation, so that children enrolled in school are more segregated than adults, which undercuts the impact of the small declines in segregation that have occurred. I conclude that racial segregation needs to be understood in terms of a broader set of factors that, operating together, limit spatial access to opportunity. 

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4. Disparate Impacts: Balancing the Need for Safe Schools With Racial Equity in Discipline

   https://doi.org/10.1177/2372732219864707  

   Johnson, Odis; Jabbari, Jason; Williams, Maya; Marcucci, Olivia (October 2019, Policy Insights from the Behavioral and Brain Sciences)

   Policy responses to gun violence within K-12 school systems have not stopped the increasing frequency of their occurrence, but have instead increased racial and ethnic disparities in multiple forms of discipline. The crisis prevention policies that follow school shootings tend to exacerbate racial and ethnic discipline disparities (a) within schools as practitioners enact policies with discretion and bias, (b) between schools where policy is complicated by racial segregation, and (c) indirectly where academic consequences accrue to those who are not disciplined but attend schools with elevated school rates of discipline. Among the most promising policy alternatives to punitive disciplinary policy is restorative justice. 

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5. MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings

   Kwon, Hyoukjun; Chatarasi, Prasanth; Pellauer, Michael; Parashar, Angshuman; Sarkar, Vivek; Krishna, Tushar (July 2020, IEEE micro)

   The efficiency of an accelerator depends on three factors—mapping, deep neural network (DNN) layers, and hardware—constructing extremely complicated design space of DNN accelerators. To demystify such complicated design space and guide the DNN accelerator design for better efficiency, we propose an analytical cost model, MAESTRO. MAESTRO receives DNN model description and hardware resources information as a list, and mapping described in a data-centric representation we propose as inputs. The data centric representation consists of three directives that enable concise description of mappings in a compiler-friendly form. MAESTRO analyzes various forms of data reuse in an accelerator based on inputs quickly and generates more than 20 statistics including total latency, energy, throughput, etc., as outputs. MAESTRO’s fast analysis enables various optimization tools for DNN accelerators such as hardware design exploration tool we present as an example. 

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