Local Indigenous perspectives and partnerships: Enhancing work-integrated learning
Bibliographic record
Abstract
Work-integrated learning (WIL) is a flourishing, global, educational phenomenon that is changing the field of higher education. Through WIL, relevant, meaningful connections to work are made throughout the curriculum that lead to enhanced graduate employability. While scholarship grows across diverse areas of WIL, one important domain that remains relatively under researched is that of Indigenous work-integrated learning (WIL). This paper launches a Special Issue in IJWIL to cultivate knowledge and practice of Indigenous WIL. It proposes a definition and design principles for those embarking on Indigenous WIL opportunities. The paper introduces twelve studies that offer insight and perspectives of Indigenous community, language and culture in a variety of contexts across Australia, New Zealand and Canada. While not every Indigenous nation, nor peoples have been represented in this Special Issue, this collection of dynamic and diverse locations and perspectives aims to ignite a global conversation. To inaugurate the special issue, the authors share an Acknowledgement of Country and statement of place, inviting others to follow in these footsteps in future research and publications of Indigenous WIL.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".