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Record W2096624227 · doi:10.1017/s0266267104001294

CRITICAL NOTICE TOO MUCH INVESTED TO QUIT

2004· article· en· W2096624227 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEconomics and Philosophy · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoase theoremTortNoticeLiabilityTransaction costLawEconomicsSocial costSet (abstract data type)Law and economicsIncentiveProperty (philosophy)SociologyPolitical sciencePhilosophyNeoclassical economicsEpistemologyMicroeconomics

Abstract

fetched live from OpenAlex

The economic analysis of law has gone through a remarkable change in the past decade and a half. The founding articles of the discipline – such classic pieces as Ronald Coase's “The problem of social cost” (1960), Richard Posner's “A theory of negligence” (1972) and Guido Calabresi and Douglas Malamed's “Property rules, liability rules, and inalienability: One view of the cathedral” (1972) – offered economic analyses of familiar aspects of the common law, seeking to explain, in particular, fundamental features of the law of tort in terms of such economic ideas as transaction costs (Coase), Kaldor-Hicks efficiency (Posner), or minimizing the sum of the accident costs and avoidance costs (Calabresi and Malamed). In each case, they argued that the law of torts should be understood as a set of liability rules selected for their incentive effects, rather than as a set of substantive rights and remedies for their violation. These authors claimed to be able to explain most of the features of tort law and, where features were found that did not fit with their preferred explanations, recommended modification. Although they disagreed on important questions, each of the pieces seems to work a manageable structure into what strikes first-year law students as an otherwise random morass of common-law judgments. Generations of legal academics were introduced to these works, and drawn into their way of looking at things. As a student studying first-year torts with Calabresi at Yale, I had the sense that I was in the presence of greatness.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.042
GPT teacher head0.230
Teacher spread0.187 · 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