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Record W4413352301 · doi:10.46697/001c.143158

International Business Education in the Age of Disruption

2025· article· en· W4413352301 on OpenAlexfundno aff
Vanessa C. Hasse

Bibliographic record

VenueAIB Insights · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsnot available
FundersIvey Business School, Western University
KeywordsBusiness

Abstract

In this Age of Disruption, characterized by the increasingly frequent occurrence of rare but impactful events, any preconceived certainties about what is known (and knowable) are eroding. This creates pedagogical conundrums for international business educators. I propose a framework which extends Bloom’s taxonomy to the dynamics of ephemeral knowledge contexts, culminating in what I call a “fire-mindset.” Six principles across three elements (Spark-Stoke-Sustain) guide the pedagogical approach, which I illustrate with a course design example. I further introduce a growing repository of original research and teaching materials. The article closes with broader reflections on the implementation across educational contexts.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: fund_new · design weight: 1678.90 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: conceptual
about Canada: no
confidence: medium

Pedagogical framework for international business education; teaching practice rather than research practice.

GPT-5.6 (high)OUT
genre: conceptual
about Canada: no
confidence: high

The article proposes a framework for international business education and course design.

Grok 4.5OUT
genre: conceptual
about Canada: no
confidence: high

Pedagogy framework for international business education addresses teaching, not research methods, evaluation, or scholarly communication.

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)0.003
metaresearch head score (Gemma)0.005
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: Commentary · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0110.016
Open science0.0010.013
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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
GenreCommentary

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

Citations0
Published2025
Admission routes1
Has abstractyes

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