Bibliographic record
Abstract
Abstract American Law in a Global Context: The Basics has long been chosen by leading law schools in the United States and across the globe for study by foreign lawyers who pursue U.S. legal education. Highly instructive for lawyers from foreign nations yet sufficiently clear and memorable for undergraduate study, American Law prepares its readers at all levels to use the case method—the dialogues between teacher and student over the law cases at the core of U.S. law study. The book features casebook-length excerpts of judicial opinions in the basic subjects of American law schools. Each case is preceded by a short, clear description of basic ideas of that subject of the law and is followed by questions and notes that help the reader grasp the case and that distinguish U.S. laws and rules from other legal systems. This much-revised edition of American Law is completely updated, with the heightened clarity required for contemporary students. It retains its comparisons to other global legal systems, focuses on easing the reader’s grasp of the basics of U.S. law and prepares those who plan to practice it. The authors apply their experiences in teaching the basics of U.S. law to thousands of students from around the world to explain points that students of all nations, including the United States, find challenging. These challenging elements of the US legal system include its Constitution, its criminal system, its juries, and its use of facts, laws, justice, and economics.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".