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Record W346474912

Taking the Costs of Consent Seriously: An Alternative Understanding of Legal Efficiency

2015· article· en· W346474912 on OpenAlexaff
Daniele Bertolini

Bibliographic record

VenueSSRN Electronic Journal · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLawmakingAllocative efficiencyProcess (computing)EconomicsPremiseOutcome (game theory)Transaction costEmpirical legal studiesLaw and economicsLawMicroeconomicsPolitical scienceLegal professionComputer scienceLegislature
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0030.033
Scholarly communication0.0150.027
Open science0.0050.007
Research integrity0.0090.009
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.073
GPT teacher head0.264
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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