A bibliographic review of work-integrated learning research
Bibliographic record
Abstract
The need to uncover the bibliographic field of work-integrated learning (WIL) stems from the increased interest to include WIL in higher education and present positive outcomes of WIL. This bibliographic review of WIL aims to understand the connectedness and trajectory of WIL in scientific publications and to explore the most influential actors. The amount of WIL research is increasing rapidly and there is global interest in the research field, even if there are dominating countries, such as Australia. From both citations, co-citations, and bibliographic coupling it is evident that there are highly influential countries, sources, publications, and authors in WIL research, which shape the bibliographic landscape of WIL. As WIL is an emerging research field, additional bibliographic reviews in coming years can show future trends in WIL research and potential differences between countries and disciplines.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.076 | 0.114 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".