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Record W2413202107 · doi:10.1002/cjas.1391

The business school scorecard: Examining the systematic sources of business school value

2016· article· en· W2413202107 on OpenAlexaffvenueabout
David Finch, Paul Varella, William Foster, Binod Sundararajan, Kim Bates, John Nadeau, Norm O’Reilly, David L. Deephouse

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsNipissing UniversityToronto Metropolitan UniversityDalhousie UniversityUniversity of AlbertaMount Royal University
Fundersnot available
KeywordsBalanced scorecardStakeholderValue (mathematics)BusinessResource (disambiguation)Stakeholder engagementAssertionBusiness modelExecutive educationBusiness caseBusiness valueKnowledge managementSociologyPublic relationsMarketingProcess managementElectronic businessComputer sciencePolitical scienceEconomicsHuman capital

Abstract

fetched live from OpenAlex

Abstract Stakeholder relationships are a critical resource that contribute to or inhibit value creation. Building on this assertion, we explore the value of the business school at a stakeholder level. We draw on research by the Canadian multistakeholder working group, the Business School Research Network (BSRN), which was established to facilitate collaborative interinstitutional research on the management and practice of business schools. We provide a conceptual model of the value chain and associated scorecard that take into account the sources of value judgments that pertain to a business school at the stakeholder‐level. Copyright © 2016 ASAC. Published by John Wiley & Sons, Ltd.

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)0.032
metaresearch head score (Gemma)0.173
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.173
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0200.028
Science and technology studies0.0020.005
Scholarly communication0.0080.006
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.060
GPT teacher head0.259
Teacher spread0.200 · 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 designObservational
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

Citations8
Published2016
Admission routes3
Has abstractyes

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