减少公立学校招生中的过滤效应：面向针对性干预的偏差感知分析
Reducing the Filtering Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions
AI总结：
本研究采用运筹学方法，通过分析纽约市教育部数据，将弱势学生分数分布偏移建模为偏差，并证明针对中等成绩弱势学生的集中干预（如奖学金或培训）可显著降低偏差影响。
摘要：
问题定义：传统上，纽约市顶尖的8所公立学校仅根据学生在特殊高中入学考试（SHSAT）中的成绩选拔候选人。这些成绩已知受到学生社会经济地位和初中所接受的考试准备的影响，导致教育管道中产生巨大的过滤效应。经典的学校分配机制并未自然解决学校隔离和班级多样性等问题，这些问题近年来日益恶化。包括政策制定者在内的科学界通过引入群体特定配额和比例约束来应对，但结果好坏参半。寻找有效且公平的方法以扩大优质教育机会的问题仍未解决。 方法/结果：我们采用与大多数现有文献不同的运筹学方法，目标是增加经济需求高的学生的机会。利用纽约市教育部（DOE）的数据，我们展示了被DOE归类为“弱势”（主要基于经济因素的标准）的学生所获分数的分布存在偏移。我们将这种偏移建模为“偏差”，源于对弱势学生真实潜力的低估。我们分析了这种偏差对分类匹配市场的影响。我们表明，当针对中等成绩的弱势学生群体时，通过奖学金或培训进行的集中干预可以显著降低偏差的影响。
英文摘要：
Problem definition: Traditionally, New York City's top 8 public schools have selected candidates solely based on their scores in the Specialized High School Admissions Test (SHSAT). These scores are known to be impacted by socioeconomic status of students and test preparation received in middle schools, leading to a massive filtering effect in the education pipeline. The classical mechanisms for assigning students to schools do not naturally address problems like school segregation and class diversity, which have worsened over the years. The scientific community, including policymakers, have reacted by incorporating group-specific quotas and proportionality constraints, with mixed results. The problem of finding effective and fair methods for broadening access to top-notch education is still unsolved. Methodology/results: We take an operations approach to the problem different from most established literature, with the goal of increasing opportunities for students with high economic needs. Using data from the Department of Education (DOE) in New York City, we show that there is a shift in the distribution of scores obtained by students that the DOE classifies as "disadvantaged" (following criteria mostly based on economic factors). We model this shift as a "bias" that results from an underestimation of the true potential of disadvantaged students. We analyze the impact this bias has on an assortative matching market. We show that centrally planned interventions can significantly reduce the impact of bias through scholarships or training, when they target the segment of disadvantaged students with average performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
