Bibliographic record
Abstract
Higher Education Institutions (HEIs) have important responsibilities for the duty of care of their students, and to ensure the wellbeing of students is kept front and center of curriculum and institutional developments. While the focus has primarily been on physical campuses, it is critical that these responsibilities extend to off campus activities, such as work-integrated learning (WIL). This special issue includes 11 articles focusing on the wellbeing of WIL students, with many authors drawing on empirical research. Key themes include students’ understanding of WIL wellbeing; students’ experiences of WIL and wellbeing; strategies for preparing WIL students to support wellbeing; understanding the wellbeing needs of diverse WIL students; and the important role of workplace supervisors in supporting WIL wellbeing. Several authors amplify the voices of students and all share thought-provoking teaching and curriculum strategies. All WIL stakeholders have a responsibility to support the learning success and wellbeing of students.
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.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".