Connecting work-integrated learning and career development in virtual environments: An analysis of the UVic Leading Edge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it