Bibliographic record
Abstract
Introduction The purpose of this paper is to develop a theory of contracts. The enterprise involved in developing such a theory needs explication, because legal theory has many branches. One branch of legal theory concerns fundamental jurisprudential issues, such as what constitutes law. Another branch concerns institutional issues, such as the nature of adjudication. However, when we talk about the theory of a specific area of law, like contracts, we mean a theory about the substantive content of the rules in that area. In this paper, I will use the terms theory of substantive law and theory of contracts in that sense. Even with this restriction, there are different conceptions of the tasks that a theory of contracts may perform. For example, the theory of contracts could be a theory of what the content of contract law is, or a theory of what the content of contract law should be. In this paper, I take the position that the primary task that a theory of contracts should perform is to provide a principle for establishing the best content of contract law, that is, a principle for establishing what the content of contract law should be. Theories of substantive law can themselves be categorized in various ways. For purposes of this paper, I distinguish between metric and generative theories of substantive law. Metric theories identify one or two variables that when properly applied result in determinate legal outcomes (or, under some theories, explain legal outcomes), in a manner somewhat analogous to scientific principles that predict determinate outcomes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".