Bibliographic record
Abstract
Abstract Some American law and economics scholars have used the term “penalty default rules” to describe default rules that are undesirable to at least one party to a contract. Parties have incentives to depart from these default rules and to share information in doing so. In a recent article, I brought this concept outside of the United States, the common law tradition and the law and economics literature by using it to describe a selection of rules in Québec contract law. In this article, I build on that work by identifying a selection of penalty default rules in three other civilian jurisdictions – France, Germany and Louisiana – that apply to contract formation, contract interpretation, changed circumstances and remedies for breach. Then, I argue that the penalty default rules that I have identified serve two valuable functions. First, they enhance at least some parties’ freedom of contract by better equipping them to make informed decisions. Second, they complement the duty of good faith by incentivizing the sharing of information, including information that might not always need to be shared in order to comply with the duty of good faith. Although these functions are somewhat different than those that law and economics scholars have attributed to American penalty default rules, my analysis reveals that penalty default rules both exist and have value in the civilian world.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".