Can students help us better rank higher education institutions? The case of French business schools
研究了利用学生入学平台数据构建揭示偏好排名的方法，以法国商学院SIGEM排名为例，分析了这种排名相对于传统学术排名的优势与局限。
Revealed preference rankings, using data from student admission platforms, present an alternative to conventional academic rankings whose reference criteria are fluctuating and manipulable. We show how admission procedures, whether centralized or decentralized, generate raw information about individual student preferences that can be exploited to construct rankings. Next, we examine the case of the SIGEM ranking of French business schools, an original example of ranking using data from a centralized admission system. This analysis contributes to understanding the strengths and limitations of revealed preference rankings in the higher education sector. While revealed preference rankings address many issues found in other ranking methodologies, some fundamental challenges still persist.