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Stakeholder Governance

2023· article· en· W4385221654 on OpenAlexaffabout
Anita M. McGahan, Sandro Cabral, Aline Gatignon, Aseem Kaul, Peter G. Klein

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility and Sustainability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCorporate governanceStakeholderBusinessPolitical scienceProcess managementPublic relationsFinance

Abstract

fetched live from OpenAlex

Scholars of strategic management, entrepreneurship, and social issues in management have recently sought to understand how stakeholder interests and stakeholder representation can be integrated into organizational governance. In this Symposium, we propose to advance research on this topic through the presentation of scholarly papers by four authors, each of whom will present recent findings developed with co-authors. The papers will be discussed formally by a leading scholar in this domain. The symposium will support scholarly dialogue among participants and the discussant in a question-and-answer session with attendees. We envision that this dialogue will advance frontier approaches on how stakeholder governance can be discerned analytically. This advancement will enhance linkages between stakeholder research and conceptualizations of governance in the fields of strategy, entrepreneurship, and social issues in management. Towards a Stakeholder-Oriented Framework on Value Creation and Allocation Author: Aline Gatignon; The Wharton School, U. of Pennsylvania Author: Julien Clement; Stanford U. Author: Luk Van Wassenhove; INSEAD Author: Leandro S. Pongeluppe; The Wharton School, U. of Pennsylvania From Social Capital to Social Justice: Racial Diversity and School Spending in US Communities Author: Farzam Boroomand; U. of Minnesota Author: Aseem Kaul; U. of Minnesota Ownership Competence and Stakeholder Governance Author: Nicolai J. Foss; Copenhagen Business School Author: Peter G. Klein; Baylor U. The New Stakeholder Theory Author: Anita McGahan; U. of Toronto

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)0.001
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 score0.738
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.054
GPT teacher head0.264
Teacher spread0.210 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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