Entrepreneurship Education in the Caribbean: Learning and Teaching Tools
Bibliographic record
Abstract
This article reports on research that took place over two academic years running from September 2013 - April 2015. It provides a rich understanding of entrepreneurship education based on experiential knowledge and best practices from five entrepreneurship educators who have all worked as consultants to entrepreneurs, advisors to government on entrepreneurship and have taught entrepreneurship at the tertiary level for several years in the Caribbean. The findings illustrate that experiences, sense of purpose, reflective practice, lecturer's passion, mentoring, simulation and practice are seen to collectively offer a significant contribution to learning. Further, the findings support the view that teachers of entrepreneurship should draw upon highly developed techniques in their range of teaching methods that demonstrate aptitude of the subject matter. The participants agreed that ideally, the ultimate course goal is to support students in remembering techniques learned in an entrepreneurship class that contribute to gaining confidence in setting up their own venture and that assist with avoiding pitfalls. The purpose of this paper is to provide methodical ways that will improve the entrepreneurial orientation of students in entrepreneurship classes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".