Champions for Change: Reconsidering Organizational Maturity in Work- Integrated Learning Partnerships
Bibliographic record
Abstract
The call for career-ready graduates is growing as the pace of change accelerates, driving demand for post-secondary institutions to enhance experiential learning. Work-integrated learning is a key intervention that contributes to developing future-ready graduates. Through a systems thinking and human-centered design lens, we explore a reconsideration of work-integrated learning approaches through an organizational maturity model for institutions in relational partnerships. This logical and rigorous analysis uses literature and a case study of a business capstone course (delivered collaboratively with an industry partner). Work-integrated learning, especially in capstone courses, is often a transactional exchange of unpaid labor for an industry-based problem where the value proposition is unequal for different stakeholders. This makes it difficult to achieve program sustainability. In this study, we offer insights for cultivating the organizational maturity to create work-integrated learning partnerships that are relational from the onset. This model highlights how mutual champions support maturity development through five phases: urgency, awareness, early activation, interconnection, and transformation. The two key moments that we focus on are navigating the decision point and the maturity chasm. We offer a translatable model with pragmatic considerations for institutions and organizations that are developing, navigating, and growing relational work- integrated learning in partnership.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".