One of the great challenges in assessing law school employment outcomes is that there seems to be some subjectivity in assessing “good” and “bad” outcomes. This could be mitigated through, say, survey data of students (e.g., “are you satisfied with your employment outcome,” etc.), but we do not have that data, and it may well reflect idiosyncratic revealed preferences of law students.
But another approach is to use a Principal Component Analysis (“PCA”). I used ChatGPT to help develop the rankings, consistent with a previous effort.
It offers a strong way to assess law school employment outcomes, and a strong contrast to the inherent weaknesses of the USNWR formula.
Human rights lawyer, Femi Falana, has accused President Donald Trump of being insincere on the death of an ISIS Commander, Abu-Bilal al-Mainuki. US President Donald Trump had announced the killing...