Evaluating Failure: A CIPP Analysis of the CriticalMas Digital Entrepreneurship Project
Bibliographic record
Abstract
This paper comprehensively evaluates the CriticalMas project, a digital entrepreneurship initiative led by the University of the West Indies Global Campus in collaboration with IBM Canada and funded by Global Affairs Canada and the Commonwealth of Learning. Aimed at empowering NEET (Not in Education, Employment, or Training) youth and final-year undergraduates across the Caribbean, the project developed two tailored curricula— Digital Heroes and Startup Academy—focused on mobile app development, business skills, and digital literacy. The project failed to be fully implemented despite strong institutional partnerships, innovative curriculum design, and substantial funding due to unresolved conflicts over intellectual property (IP) ownership. This paper uses the CIPP (Context, Input, Process, Product) evaluation model to analyze the CriticalMas project's trajectory, highlighting its valuable educational outputs and the institutional policy misalignments that led to its termination. The project's findings underscore the critical significance of aligning IP frameworks with the goals of entrepreneurship education, particularly when targeting marginalized populations whose empowerment depends on ownership of their creative work.
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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 | 0.050 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".