Bibliographic record
Abstract
A contract is an artifact that records an agreement made by the parties of the contract. Although contracts are considered to be legally binding and can be very complex, they are usually expressed in an informal language that does not have a precise semantics. As a result, it is often not clear what a contract is intended to say. This is particularly true for contracts, like financial derivatives, that express agreements that depend on certain things that can be observed over time such as actions taken of the parties, events that happen, and values (like a stock price) that fluctuate with respect to time. As the complexity of the world and human interaction grows, contracts are naturally becoming more complex. Continuing to write complex contracts in natural language is not sustainable if we want the contracts to be understandable and analyzable. A better approach is to write contracts in a formal language with a precise semantics. Contracts expressed in such a language have a mathematically precise meaning and can be manipulated by software. The formal language thus provides a basis for integrating formal methods into contracts. This paper outlines a formal language with a precise semantics for expressing general contracts that may depend on temporally based conditions. We argue that the language is more effective for writing and analyzing contracts than previously proposed formal contract languages.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".