Bibliographic record
Abstract
Throughout the twentieth century, elite US law schools have been presented as sites of power, admiration, influence and envy. Robert Stevens, in the opening of his seminal 1983 work Law School, suggested that foreign lawyers looked wistfully at elite US law schools. At a time when US political institutions—and even law schools—seem to have lost much of their former global luster, this book investigates whether in reality the elite US models ever proved so attractive to foreigners. Collectively the contributions cast doubt on traditional narratives that point toward the globalization or homogenization of legal education. They challenge the idea that many educators beyond the United States believed that the adoption of American models would lead to better legal education and scholarship, better legal systems, better lawyers, and better governance. And they illuminate the cultural and political significance of attempts to transplant US models. The book consists of historical examinations of American contacts within legal education in fourteen countries: China, Japan, Israel, the Philippines, Nigeria, Kenya, Ghana, France, Brazil, Sweden, Estonia, England, Australia, and Canada. And it includes critical commentary from two leading American law professors, along with a founding chapter from Bruce Kimball, the leading historian of Harvard Law School.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.015 |
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".