Taking Entrepreneuring Seriously in Entrepreneurship Education—Conceptual Dimensions and Implications for the Classroom
Bibliographic record
Abstract
The scholarly and educational field of entrepreneurship has rapidly expanded and evolved over time, carrying promises of enhanced economic growth and prosperity for nations that harness it. Such speedy establishment and expansion of entrepreneurship education, however, may have obscured the onto-epistemological underpinnings of our pedagogical methods and students’ learning. Thus, the following questions may need attention: Why does entrepreneurship education need to be refocused? Under what onto-epistemological rationale should such renewal be brought forward? And what could be gained for the field of entrepreneurship education? This conceptual paper opens discussions about these three questions from the perspective of entrepreneuring. Entrepreneuring with its underlying process and practice onto-epistemology may be a propitious “conceptual attractor” for the classroom, bringing forward four key pillars for entrepreneurship education to be taken into account: (1) situatedness in space and time; (2) relatedness and open-endedness; (3) everyday creativity and play; (4) reflexivity. The paper contributes to entrepreneurship education by proposing specific learning objectives, teaching methods, and assessment practices for each of the aforementioned four pillars and invites educators to consider entrepreneuring, social and more mainstream perspectives of entrepreneurship not as exclusive but as integrative perspectives that can be unified under the “socializing” umbrella of entrepreneuring.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".