Templatization Without Homogenization: Entrepreneurship Frameworks in Undergraduate Classrooms
Bibliographic record
Abstract
Theories of management fashion predict that mass diffusion of a single framework can foster isomorphism or settlement on a common approach. Thus, frameworks codified and distilled into templates should produce cohesive replication over time. Yet, little work has examined how management fashions, and in particular, fashions embedded in templates, shape what scholars teach. We address this gap by focusing on the growing field of undergraduate entrepreneurship, where common templates are increasingly available and influential. We ask: What is taught in undergraduate entrepreneurship classrooms? With a survey of 86 syllabi at 84 US colleges and universities, we observe templatization without homogenization: a) widespread adoption of common templates, but b) little evidence of the standardization that might be expected. With interviews of 23 educators, we unpack this juxtaposition. Our analysis suggests this pattern arises from educators’ individual efforts to grapple with field level tensions concerning intended learning outcomes, topic scope, and effective pedagogical strategy. Overall, our research contributes an underappreciated lens as to how educators leverage templates and frameworks to manage an expanding mandate for entrepreneurship while stubbornly resisting standardization.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".