MétaCan
Menu
Back to cohort
Record W2093143504 · doi:10.3138/utlj.60.2.467

ACADEMIC SCRIBBLERS AND DEFUNCT ECONOMISTS

2010· article· en· W2093143504 on OpenAlexvenueno aff
Robert D. Cooter, Hans Bernd Schaefer

Bibliographic record

VenueUniversity of Toronto Law Journal · 2010
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsDilemmaInnovatorState (computer science)Institutional economicsLiberalizationNew institutionalismCoase theoremEconomicsBusinessLaw and economicsMarket economyEntrepreneurshipPolitical scienceNeoclassical economicsFinanceLawTransaction costPolitics

Abstract

fetched live from OpenAlex

Three broad theoretical approaches characterize the history of development economics: state-led growth, which dominated from the 1930s until roughly 1980; liberalization theory (the ‘Washington Consensus’), which dominated in the 1980s; and institutionalism, which dominated subsequently. After 2000, development scholars increasingly focused on the legal institutions that support markets: property, contracts, and business law. This essay focuses on legal institutions that support economic innovation, which causes sustained growth. Developing countries mostly innovate by discovering new markets and adapting organizations, which is risky. A risky venture that unites capital and new ideas poses a problem of trust: the investor must trust the innovator not to steal his money, and the innovator must trust the investor not to steal his ideas. If institutions supply the means for entrepreneurs to solve the double trust dilemma, the economy strides ahead through innovation. Conversely, if institutions fail to supply the means for entrepreneurs to solve the double trust dilemma, the economy hobbles along without improvement. The origin of a nation's laws – whether common or civil law – is not so important as the ability to adapt law to business innovations.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.175
Teacher spread0.168 · 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 teacher head, 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueUniversity of Toronto Law JournalSame topicEconomic Growth and DevelopmentFrench-language works237,207