Bibliographic record
Abstract
What is a good legal education? This is a question that both blesses and blights us all - it blesses us because it forces us to keep on our toes and should constantly makes us challenge what we are doing and what we take for granted, it blights us because there is no likely consensus on what it might mean and tends to pull us apart rather than bring us together. In addressing this perennially perplexing question, I want to ask how we might go about answering it (or, at least, framing the issues) when it comes to dealing with matters of race and racial diversity in student admissions. The general perception of the problem is that, while greater diversity in the student population is to be encouraged, it must not occur at the price of lower standards in the delivery of legal education. For me, this is precisely the kind of dichotomous thinking that must be done away with. It is not a choice between broadening access and preserving quality - access is a dimension of quality, they go together, not pull apart. In short, I want to take a controversial and unconventional line that recommends that law schools, if they are to provide a good legal education, must embrace affirmative action in their admission practices. It is my contention that there is no connection between broadening access and diluting standards. On the contrary, on the view of a good legal education that I propose, greater ethnic diversity will enhance the quality of legal education delivered and received.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.017 | 0.057 |
| Scholarly communication | 0.032 | 0.037 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".