The role of work-integrated learning in the development of entrepreneurs
Bibliographic record
Abstract
This study explored the ways that work-integrated learning (WIL) influences the development of entrepreneurs. \nSeven alumni from Canada and the United Kingdom, who experienced differing forms of WIL during their degree, \nparticipated in qualitative in-depth interviews and provided rich pictures. A rich picture is a pictorial \nrepresentation of a situation, including what happened, who was involved, how the participant perceived the \nsituation. During the interviews, participants reflected on how WIL impacted their career and they created rich \npictures to depict their perception of an entrepreneur and what influenced them to become an entrepreneur. \nSeveral important themes emerged and included seizing opportunities, thinking “outside the box,” being resilient \nduring difficult times, and the importance of networks. The influence of WIL was important for all participants \nand provided the framework of support that enabled the participants to manage difficult times and turn disruption \ninto opportunity.
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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 | 0.005 | 0.008 |
| 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 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 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".