Bibliographic record
Abstract
Ex post gap filling is a central function of contract law.This is about to change.Predictive capabilities created by big data and artificial intelligence increasingly allow parties to draft contracts that fill their own gaps and interpret their own standards without adjudication.With these self-driving contracts, parties can agree to broad objectives and let automated analytics fill in the specifics based on real-time contingencies.Just as a selfdriving car fills in the driving details to get its passenger to a designated end point, the self-driving contract fills in the contract details to achieve the parties' designated outcome.This development suggests a new focus for the doctrine and theories of contract law.Our primary goal in this Article is to introduce and develop that new focus.For example, self-driving contracts are both complete and incomplete.They are complete in that they specify actions for every contingency.This reduces the likelihood of breach and renegotiation.It also means that notions of efficient breach and ex post hold-up will be of reduced importance in contract law.At the same time, self-driving contracts are also incomplete in ways that render current notions ofdefiniteness and mutual assent irrelevant or at best misleading.Perhaps most importantly, with contracts being interpreted by their own internal software, contract law will have to focus on where that software comes from and how it operates.Markets will arise for third-party vendors who either certify or provide independent contract programming.In some cases, these will be new markets; in others, they will evolve from existing markets such as the market for contract arbitrators.Law will play a role in supporting and overseeing these markets.We explore that role, and how it will differ in markets for contracts between sophisticated parties and in markets for consumer contracts.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.009 |
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".