MétaCan
Menu
Back to cohort
Record W3142750099 · doi:10.3386/w23460

Governance and Stakeholders

2017· report· en· W3142750099 on OpenAlexaff
Vikas Mehrotra, Randall Mørck

Bibliographic record

VenueNational Bureau of Economic Research · 2017
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCorporate governanceBusinessAccountingProcess managementFinance

Abstract

fetched live from OpenAlex

Economic models routinely assume firms maximize shareholder wealth; however common law legal systems only require that officers and directors pursue the interests of the corporation, leaving this ill-defined.Economic arguments for shareholder wealth maximization derived from shareholders' status as residual claimants are vulnerable on several fronts.Share valuations fluctuate as sentiment shifts.Introductory finance casts firms as maximizing expected net present values, which are quasirents, expected earnings beyond expected costs of capital from investors, to which shareholders have no obvious claim.Other stakeholders -entrepreneurial founders or CEOs, employees, employees, customers, suppliers, communities or governments, having made firm-specific investments, may exert stronger claims than atomistic public shareholders have to shares of their firms' quasirents.Consistent with this, their contractual claims are often augmented by residual claims and liabilities.Still, shareholder value maximization constitutes something of a bright line; whereas stakeholder welfare maximization is an ill-defined charge to assign boards that gives self-interested insiders broader scope for private benefits extraction.The common law concept of "the interests of the corporation" captures this ambiguity.

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)0.005
metaresearch head score (Gemma)0.010
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: Other
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.009
Scholarly communication0.0070.007
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.514
GPT teacher head0.447
Teacher spread0.068 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueNational Bureau of Economic ResearchSame topicLaw, Economics, and Judicial SystemsFrench-language works237,207