Taking the Costs of Consent Seriously: An Alternative Understanding of Legal Efficiency
Bibliographic record
Abstract
Most law and economics literature employs efficiency criteria that fit poorly with the structural features of the legal environment. The major limitations trace back to the analytical separation of law from its formative process, which has resulted in an almost exclusive focus on the allocative efficiency of legal entitlements and little or no attention paid to the causal relationship between the efficiency of legal rules and the efficiency of the lawmaking process. I contend that this conventional output-oriented approach is susceptible to the following criticisms: 1) it is affected by logical circularity and/or logical incompleteness; 2) it fails to provide any assurance of increased social welfare, 3) it does not account for the presence of losers, and 4) it does not account for the predictability/adaptivity trade-off associated with legal change.Based on the foregoing considerations, this paper proposes an alternative understanding of legal efficiency. Efficiency is not an objective property of the outcome independent of the process; rather, it depends on the ability of the law-making process to embody, in a cost-effective manner, the general consensus of all the people concerned. Based on this premise, this paper proposes a methodology focused on the “process-outcome” relationship within the production of law, which I call “process efficiency analysis”. It relies on the analytical tools offered by transaction-cost economics and is grounded in the normative principles of constitutional contractarianism. In the last section of the paper, I illustrate process efficiency analysis by using an example from tort law.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| 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".