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Yunxiao Chen; Chengcheng Li; Jing Ouyang; Gongjun Xu – Grantee Submission, 2023
We consider the statistical inference for noisy incomplete binary (or 1-bit) matrix. Despite the importance of uncertainty quantification to matrix completion, most of the categorical matrix completion literature focuses on point estimation and prediction. This paper moves one step further toward the statistical inference for binary matrix…
Descriptors: Statistical Inference, Matrices, Voting, Federal Government
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Jeremy Singer – Race, Ethnicity and Education, 2024
In education, low-income and racially minoritized students in urban districts are often constructed as 'dependent' -- weak in their social positions but deserving of educational opportunity. This social construction of 'urban' students has been central to school choice politics and policymaking in the United States. In this study, I interrogate…
Descriptors: Urban Education, School Choice, Urban Schools, Low Income Students
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Heather McCambly; Stephanie Aguilar-Smith – AERA Open, 2024
Troubled by the inequities in competitive grantmaking, we use critical quantitative methods to analyze the FY2023 federal academic earmarks as a potential mechanism for racialized change work. Specifically, we ask: To what extent does Congress distribute academic earmarks in ways that reinforce or weaken the racialized stratification of resources…
Descriptors: Higher Education, Educational Policy, Equal Education, Federal Aid
US House of Representatives, 2022
This document records testimony from a hearing before the Committee on Education and Labor that was held to examine ways to improve accountability and prevent fraud in for-profit college conversions. Member statements were provided by: (1) Honorable Robert C. Scott, Chairman, Committee on Education and Labor; and (2) Honorable Virginia Foxx,…
Descriptors: Private Colleges, Prevention, Legislators, Accountability