The Development of a Proposed Global Work-Integrated Learning Framework.
Bibliographic record
Abstract
Building on the work completed in [British Columbia] BC that resulted in the development of a [work integrated learning] WIL Matrix for comparing and contrasting various forms of WIL with the Canadian co-op model, this paper proposes a Global Work-Integrated Learning Framework that allows for the comparison of a variety of models of work-integrated learning found in the international post-secondary education system. The Global Framework enables researchers, practitioners, and other WIL stakeholders including students and employers to better understand the key goals and outcomes of each model as well as explore the commonalities and differences between the various models based upon identified attributes of quality experiential education programs. This Framework also provides a means for situating or developing new models of WIL intentionally designed for specific experiential learner outcomes and program impacts. At the institutional level, the Framework provides a mechanism for rationalizing the many, and often independently designed and delivered, WIL offerings by connecting them through their shared attributes and providing a way to differentiate them through their unique processes and outcomes. The proposed Framework is based upon high impact practices for experiential learning as identified in the literature and allows users to map WIL programs directly to the academic agenda through learning outcomes.
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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 | 0.008 | 0.005 |
| 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 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".