The Unstated Ontology of the Business Case Study: Listening for Indigenous Voices in Business School Curricula
Bibliographic record
Abstract
In this work, we examine how business case studies can support decolonial Indigenization efforts in business schools. Through our inductive analysis of business case studies from top publishers in this area (Harvard Business Publishing and Ivey Publishing), we identify a set of ontological assumptions that undergird the construction of traditional, Western business case studies and ultimately undermine Indigenous ways of knowing and organizing. Specifically, we find that the case studies stem from an uncritical Western ontology, particularly in relation to the construct of “development,” which diminishes the positionality of Indigenous Peoples in our own stories. We demonstrate how case studies can support decolonial Indigenization by crafting them in ways that uphold Indigenous ontologies. Finally, we uncover how a reconsideration of case writer positionality, Indigenous “voice” in business education, and Indigenous conceptions of development can enable case crafting in service of decolonial Indigenization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.067 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".