Bibliographic record
Abstract
Private ordering – where private actors regulate, enforce, and resolve disputes on their own – has in recent years expanded across business, commercial, and financial sectors. Parties have economic and reputational incentives to take this approach over adjudication by the courts. Parties may prefer private ordering for reasons of process, substance, or both. Even when disputes come before them, courts often defer to parties’ private ordering. Their rationale is that the parties possess a stronger understanding of their intentions than do the courts. This strong assumption, however, depends on parties’ knowledge and relative bargaining strength. In many instances, parties operate under incomplete or imperfect information; additional information could allow parties to enter into more efficient and more fair agreements ex ante, while better informing courts’ approach to adjudicating disputes arising from private ordering ex post. The emergence of artificial intelligence (AI) in legal technology – specifically, in its ability to analyse vast amounts of data – can help advance this augmented informational objective. If made broadly accessible, AI has the potential to equalize information and bargaining power between parties. An empirical evaluation of the validity of assumptions that underpin the general support for private ordering can also be instructive for judges. For this reason, courts have an important role to play in the evolution of private law. Their ability to understand and harness AI can lead in the short term to more effective judicial oversight with respect to private ordering. Over the long term, courts can empower parties to make more informed choices when interacting with one another, reducing inefficiencies and rents.
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 | 0.114 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 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".