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Record W2528660797

Economic Efficiency, Normative Account-Giving and Corporate Law

2015· article· en· W2528660797 on OpenAlexaff
QC Bryce Tingle

Bibliographic record

VenueSSRN Electronic Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNormativeCorporate lawCorporate governanceLegitimacyRealmShareholderLaw and economicsEconomic JusticePolitical scienceLawState (computer science)EconomicsBusinessPoliticsManagement
DOInot available

Abstract

fetched live from OpenAlex

Professor Marc T. Moore's Corporate Governance in the Shadow of the State, addresses the recent regulatory interventions in Anglo-American corporate law. The book attempts to take the debate about these legal changes out of the usual realm of arguments about their efficiency (where they can score poorly) and into the normative question of how corporate law can legitimate the power managers have over the resources of shareholders. Moore's normative approach is so good, that its ultimate weaknesses have the effect of calling into question the entire project of privileging normative criteria like legitimacy or justice over corporate law's traditional emphasis on securing economically efficient outcomes.

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)0.002
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.290
Teacher spread0.261 · 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
Published2015
Admission routes1
Has abstractyes

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