Is Entrepreneurship Education Only about Entrepreneurship?
Bibliographic record
Abstract
An important and unexamined issue in the study of entrepreneurship education (EE) and its outcomes concerns understanding the role of students in defining the outcomes of entrepreneurship education. Drawing on the narrative interviews with 31 students of educational programs, we identify that not all students join EE programs to become entrepreneurs and many students have the objective of becoming management professionals. Students with these decision frames engage themselves in constructing their respective professional identities: entrepreneurial and managerial. Professional outcomes achieved by those students either resulted in work- professional identity integration or work- professional identity coherence violation. Students were able to enact their professional identity in the situation of work- professional identity integration and work- professional identity coherence violation triggered identity redefining and/or constructing dual identity ( managerial and entrepreneurial). Based on these findings, we develop a theoretical model of students’ experiences of constructing, enacting and redefining their professional identities during and after the program and contribute to the literature of entrepreneurship education and identity.
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.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".