Revisiting the Many Legal Institutions that Support Contractual Commitments in a Globalized World
Bibliographic record
Abstract
Abstract Neoclassical economic models assume the enforcement of contracts to be costless and automatic. In practice, litigation and adjudication subject contract enforcement to high transaction costs. The characteristics of the local legal environment and the structure of available legal institutions affect the transaction costs of contract enforcement. Empirical and theoretical literature are beginning to disentangle the contributions that distinct formal legal institutions make in securing contractual commitments at low cost. Well-specified private law and efficient rules of procedure contribute to low-cost enforcement. So do competitive markets for legal talent and a judiciary that has incentives to produce fair and efficient outcomes. Increasingly, institutions outside of the local jurisdiction, including predictable rules of private international law, well-structured competition between courts, and innovations in online dispute resolution, determine enforcement costs. Interdependencies between domestic and transnational legal institutions suggest that well-functioning local courts and legal professions will no longer be sufficient in guaranteeing low-cost access to contract law. Interjurisdictional cooperation and transnational institutions are needed to secure contractual commitments in a globalized world.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".