Challenges of Developing and Sustaining a Research Partnership for Work Integrated \nLearning
Bibliographic record
Abstract
Challenges of Developing and Sustaining a Research Partnership for Work Integrated Learning \nBack in 2007 at WACE in Singapore, Zegwaard reported that in 1997 research into Work Integrated Learning (WIL) had been ‘limited, uncertain and sparse’. Things had improved slightly in 2004 where Bartkus and Stull said ‘What has been published is good’. Things have moved on in recent years with the growth of a much larger WIL research community. Much of this has been supported by WACE members who have organised research events around world conferences and symposia, and also with the re-invigoration of two specialist journals. Yet in many ways WIL is still an emerging research field. \nThis paper will explore some of the practicalities of developing and sustaining a research partnership for WIL. The author will discuss getting started in research, building knowledge in the field and developing resources for WIL. At a local level, difficulties faced by researchers include managing projects alongside busy ‘day jobs’ and maintaining continuity as team members change. At a global level, the most significant challenge is ensuring a partnership approach by developing working relationships across international boundaries. \nConsideration will be given to how to set up a research team, and to the challenges of working across disciplines as Deborah Peach et al (2011) explore further in their article on boundary spanning. The paper will also explore issues of sustainability, including funding models – in particular, the continual search for financial resources to sustain ongoing research. The author will conclude with reflections on issues of team and interdisciplinary working. \n \nKeywords: \nWIL Research Partnerships Funding Resources
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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 | 0.198 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.034 | 0.030 |
| Scholarly communication | 0.047 | 0.055 |
| Open science | 0.011 | 0.060 |
| Research integrity | 0.022 | 0.026 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".