Protecting Individual Self-Interest in Aggregate as the Basis of Fairness in Contract
Bibliographic record
Abstract
This article puts forward a unifying principle for the exceptions to contractual enforcement, including unconscionability, undue influence, duress, and mistake. In coming to a unified analysis, this article explains and defends three general premises. First, contract law should be understood as operating to maximize societal welfare in the aggregate. Second, contractual enforcement encourages and enforces welfare-enhancing agreements, but only if we can assume that each party is able to rationally consider her own self-interest. Third, agreements that were rationally welfare-enhancing when made should be enforced later even in cases of regret. Based on these premises, the analysis of unenforceability of unfair contracts can be reduced to two questions: whether, in the circumstances, parties to a contract were rationally able to consider and protect their self-interest, and, if not, whether the other side knew or ought to have known this.
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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 | 0.030 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".