Bibliographic record
Abstract
I examine the case where fulfillment of a contractual commitment is only imperfectly verifiable and ask whether the court should then "tell the truth"" regarding the action in dispute. I show that truth seeking does not maximize the expected surplus from contractual relationships. From the parties' viewpoint, the enforcer should disregard some of the available information and should sometimes rule in favor of one party, even though his understanding is that the other party is most probably right. The analysis provides a justification for rules of evidence in common law and for the use by courts of neutral normative priors regarding contending claims." J'analyse le cas où la réalisation d'un engagement contractuel n'est qu'imparfaitement vérifiable. La question posée est de savoir si le tribunal doit alors « dire le vrai » quant aux actions faisant l'objet d'un litige. Je montre que, du point de vue des contractants, la cour devrait faire abstraction d'une partie de l'information disponible et qu'elle devrait parfois statuer en faveur d'un des contractants, même si elle considère plus probable que l'autre ait raison. Cette analyse fournit une justification à certaines règles de procédure en droit civil et elle justifie le recours à des a priori normatifs neutres dans le règlement des différends.
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 | 0.039 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.028 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".