Introduction: Welcome to the Study of American Law
Bibliographic record
Abstract
Abstract From Sweden to South Africa, Beijing to Buenos Aries, Vancouver to Victoria, and across the United States, American Law in a Global Context: The Basics has been studied in leading universities and law schools to help students learn U.S. law and adapt to the unique learning environment of U.S. law schools. Whatever your background, American Law helps you leverage your prior knowledge to more easily understand U.S. law. To prepare you for the famous—but confusing—“case method,” this chapter introduces ten basic ideas that will help you to understand the “common law” as it is taught in U.S. law schools and to “think like a lawyer”: (1) Most American law is state law, not national law. (2) Most first-year reading is state law. (3) Citizen juries influence the U.S. legal process. (4) U.S. courts are highly decentralized, and U.S. legal officials are remarkably independent. (5) Each state has two unified court systems, one state and one federal. (6) The United States has few specialized courts. (7) Most law school readings are appellate judicial opinions. Most U.S. courts have general jurisdiction, not a legal specialization. (8) Both federal and state appellate systems have two levels of appeals. (9) Appeals rarely correct facts but review for mistakes of law, particularly arising from substantive law, pleadings, evidence, and jury instructions. (10) You should study cases to learn the methods that lawyers use to make arguments and judges use to resolve disputes. It is not enough to simply remember legal rules, because class time aims to prepare you to discuss and argue the law, not to recite the opinion.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.155 | 0.061 |
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".