Doctoral Programs in Entrepreneurship: Building Cognitive Apprenticeships
Bibliographic record
Abstract
This paper applies a supply-side perspective to entrepreneurship education research and explores the socialization process for students in entrepreneurship doctoral programs in the United States (US). It presents the challenges facing higher education regarding how academia prepares future professors to teach. The paper proceeds to build a new model by integrating concepts of cognitive apprenticeship with theories of socialization and considers how it can be used to address these concerns. Our research questions explore different types and stages of socialization, and our theory development presents a combined framework integrating socialization with cognitive apprenticeship. The paper then introduces the methodology for the study, a tripartite design that uses marketing documents/websites, as well as behavioral event and perception interviews. It discusses the results of the data as they relate to an ideal cognitive apprenticeship model. The work illustrates that there is much to do to improve educator development in doctoral programs. We discuss the conceptual contributions of our work illustrating the value of our model in entrepreneurship education, as well as highlighting its value in other research contexts. Seven recommendations are presented, intended to help enhance the way US programs in entrepreneurship embed the cognitive apprenticeship of educators into the learning process for future professors. Our primary contribution is to demonstrate how a cognitive apprenticeship model can be used to address entrepreneurship educator development concerns in doctoral programs, while avoiding significant changes or unreasonable investments in existing programs.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".