Embedded entrepreneurship pedagogy: Six key practices for an entrepreneurial classroom
Bibliographic record
Abstract
• Modern employment expectations demand new educational perspectives. • Skills and competency education models show promise as responsive pedagogies. • Youth entrepreneurship education shows great potential for building key knowledge, skills, and attitudes, including creativity, persistence, risk tolerance, collaboration, and knowledge synthesis. • Embedding entrepreneurial ideas and practices into secondary school course offerings can promote entrepreneurial propensity. • Six key practices can be integrated into various curricular areas with the intention of building entrepreneurial competency among youth. This paper explores youth entrepreneurship education, most suitable at the secondary school level, through the mobilization of embedded entrepreneurship pedagogy. The paper is informed by a two, recently completed research projects, involving classroom teachers, and practicing entrepreneurs. Those projects, which examined how secondary schools can sharpen their entrepreneurial offerings to students, and in addition to their primary findings, hinted at the possibility of infusing key entrepreneurial principles into all curricular learning experiences. To illustrate this approach, this paper will examine how embedded entrepreneurship education can be included as part of the curricular and pedagogical planning of any subject area. It introduces six key practices for embedded entrepreneurship and explores, with examples, their curricular and cross-curricular potential.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".