Connecting work-integrated learning and career development in virtual environments: An analysis of the UVic Leading Edge
Bibliographic record
Abstract
While the fields of work-integrated learning (WIL) and career development share common goals, WIL literature tends to focus on student employability more than students' ability to manage their careers. The Leading Edge program at a Canadian institution, the University of Victoria, brings together these two disciplines as it draws from theory and methodology in WIL and career development to strengthen student experiential learning and prepare students for meaningful careers. Four reflective questions form the core of the program, and support students to become pro-active experiential learners, embrace diversity and become career-ready during their academic journey. The authors present the theoretical underpinnings in career development, WIL and experiential learning that inform the program development, and analyse its strengths and challenges. The paper concludes with an exploration of how the Leading Edge, an online program, can support learners to navigate the challenges of the current labour market conditions created by [Coronavirus Disease 2019] COVID-19.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".