内容与形式：不同社会经济背景下写作分数差距的驱动因素？一种生成面板方法
Content vs. Form: What Drives the Writing Score Gap Across Socioeconomic Backgrounds? A Generated Panel Approach
AI总结：
本文通过生成面板方法研究社会经济地位对写作分数差距的影响，发现内容质量占69%，风格差异占26%，评估标准差异占5%。
摘要：
不同社会经济背景的学生在测试分数上表现出持续的差距，这些差距可能转化为不平等的教育和劳动力市场结果。在许多评估中，表现不仅反映学生所知的内容，还反映他们有效传达知识的能力。这种区别在写作评估中尤为突出，因为分数同时奖励学生想法的实质和表达方式。因此，观察到的分数差距可能将底层内容差异与表达技能差异混淆。因此，一个核心问题是，社会经济地位（SES）在分数差距中的多少是由学生所说的内容差异还是他们如何表达的差异所驱动的。我们使用大量说服性文章的语料库研究了这个问题，这些文章由美国中学生和高中生撰写。我们引入了一种新的测量策略，通过利用大型语言模型生成每篇论文的多个风格变体来区分内容与风格。这些重写保留了底层论点，同时系统地改变表面表达，创造了一个
英文摘要：
Students from different socioeconomic backgrounds exhibit persistent gaps in test scores, gaps that can translate into unequal educational and labor-market outcomes later in life. In many assessments, performance reflects not only what students know, but also how effectively they can communicate that knowledge. This distinction is especially salient in writing assessments, where scores jointly reward the substance of students' ideas and the way those ideas are expressed. As a result, observed score gaps may conflate differences in underlying content with differences in expressive skill. A central question, therefore, is how much of the socioeconomic-status (SES) gap in scores is driven by differences in what students say versus how they say it. We study this question using a large corpus of persuasive essays written by U.S. middle- and high-school students. We introduce a new measurement strategy that separates content from style by leveraging large language models to generate multiple stylistic variants of each essay. These rewrites preserve the underlying arguments while systematically altering surface expression, creating a "generated panel" that introduces controlled within-essay variation in style. This approach allows us to decompose SES gaps in writing scores into contributions from content and style. We find an SES gap of 0.67 points on a 1-6 scale. Approximately 69% of the gap is attributable to differences in essay content quality, Style differences account for 26% of the gap, and differences in evaluation standards across SES groups account for the remaining 5%. These patterns seems stable across demographic subgroups and writing tasks. More broadly, our approach shows how large language models can be used to generate controlled variation in observational data, enabling researchers to isolate and quantify the contributions of otherwise entangled factors.
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。
