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Employees as Corporate Stakeholders

2002· article· en· W2030312961 on OpenAlexaffabout
Poonam Puri, Tuvia Borok

Bibliographic record

VenueJournal of Corporate Citizenship · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsYork University
Fundersnot available
KeywordsCorporationShareholderStakeholderBusinessContext (archaeology)Stakeholder theoryPoliticsPublic relationsStatuteShareholder primacyCorporate social responsibilityWork (physics)Corporate governancePolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

Enron offers a prime case study to explore the many failures of the modern corpora-tion to treat its employees even-handedly, ethically and with respect. Employees make significant personal investments into the corporations they work for, but employee interests are rarely of concern to directors. This paper analyses several theories on the nature and purpose of the corporation and argues that the interests of stakeholders other than shareholders ought to be legally recognised in the corporation. The paper examines three theories on employees and employment, and argues that the human capital theory is most meaningful and best reflects the role of the employee when that employee is analysed in a broad social, political and economic context. The authors evaluate reforms, such as stakeholder statutes, which have been implemented in various jurisdictions, and set out specific amendments that ought to be implemented to recognise the status of the employee as an important corporate stakeholder in the modern corporation. Tuvia Borok is a graduating law student of Osgoode Hall Law School in Toronto, Canada. This autumn, Tuvia will pursue his master’s of law abroad focusing on the study of global corporate markets. He is scheduled to return to the firm of Osler, Hoskin & Harcourt in Toronto, Canada, after his graduate studies.

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 categoriesInsufficient payload (model declined to judge)
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 score0.505
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.150
GPT teacher head0.230
Teacher spread0.080 · 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.

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

Citations1
Published2002
Admission routes2
Has abstractyes

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