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Record W1540310812

Producing Law for Innovation

2010· article· en· W1540310812 on OpenAlexaff
Gillian K. Hadfield

Bibliographic record

VenueTSpace · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBusinessProduction (economics)Corporate governanceState (computer science)Legal professionLaw and economicsIndustrial organizationLawFinanceEconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In this chapter, I first discuss why we need to think of legal infrastructure as economic infrastructure requiring focused economic policymaking, what is wrong with our existing legal infrastructure and why we need to change our modes of legal production. I then set out a vision of what greater reliance on market-based production of legal infrastructure could look like. Finally, I suggest some concrete steps that policymakers can take to move us toward a more open, competitive system of legal production. These include 1) opening up access to the provision of legal services, such as by establishing a federal licensing regime that exempts providers from state-based regulation by the bar and state supreme courts and reduces restrictions on the ownership and management of legal providers; 2) establishing the public law framework necessary to enable the emergence of competitive private legal entities to supply legal rules (for corporate governance and commercial contracting, for example); and 3) reducing barriers to international trade in legal services.

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)0.011
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0050.019
Scholarly communication0.0130.015
Open science0.0020.008
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0320.007

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 head0.045
GPT teacher head0.270
Teacher spread0.225 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations4
Published2010
Admission routes1
Has abstractyes

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