Entrepreneurship education and training in Indian higher education institutions: a suggested framework
Bibliographic record
Abstract
Purpose The study aims to evaluate the components of entrepreneurship education and training (EET) in India. The paper proposes a framework for an effective EET regime for amalgamating entrepreneurship education as fundamental to mainstream higher education in India. Design/methodology/approach The current study utilises a qualitative research technique, that is, the narrative inquiry methodology based on in-depth interviews. The study respondents included sixteen educators who are actively engaged in EET and related activities for a minimum of ten years. Findings The study identified five broad “meaning units” or “themes,” that is, “incremental pedagogical efficiency and flexible evaluation systems,” “entrepreneurial experience of the faculty,” “extended support,” “holistic mentoring” and “experiential learning” as components of an effective EET regime. Originality/value The study will help the policymakers and higher education institutions (HEIs) revisit their policy frameworks and practices to promote entrepreneurial capacity and entrepreneurial intentions among students. The study will also help to gain deeper insights into EET components and will propose a framework for an effective EET regime based on its findings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".