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Record W4321456019 · doi:10.1177/10525629231154891

Grand Challenges and the MBA

2023· article· en· W4321456019 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOrganizational Behavior Teaching Review · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsWestern University
Fundersnot available
KeywordsSalience (neuroscience)Grand ChallengesHumanityPublic relationsEngineering ethicsAuditSociologyPolitical sciencePsychologyManagementEngineeringEconomics

Abstract

fetched live from OpenAlex

Humanity is facing multiple grand challenges, compelling a myriad of diverse actors to interact, coordinate, and collaborate like never before. Business schools have a role to play in equipping future leaders to tackle them and we posit that to do so, leaders must be able to take multiple perspectives into consideration and look to the future while being morally aware. We carry out an in-depth audit of how MBA programs currently fare in this regard. We find that despite the urgency and salience of these transnational and intractable issues, little attention is paid to preparing MBA students to address grand challenges. We identify three barriers that may prevent educators from facilitating student acquisition of these competencies and conclude by proposing potential models of MBA programs for grand challenges.

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.

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)0.009
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.578
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.259
GPT teacher head0.442
Teacher spread0.183 · 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