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
The demand for work-ready graduates, who are familiar with organizational practices in the workplace is increasing, and so the need for greater work integrated learning (WIL) is a growing concern for the education sector. With the globalization of higher education and the cultural and linguistic challenges this brings, WIL has become a core strategic issue for many organizations. Examining WIL as a process of integration between workplaces, higher education institutions, government, business and industry, this book includes: Strategies for managing work integrated learning experiences The what, when, where, why and who of WIL across professions Advice on building relationships between higher education and the workplace Guidance on preparing learners effectively for work Practical case studies from firsthand experience Direct information and instruction on the use of WIL Work Integrated Learning is a practical guide that can be used by the education sector and employers alike. An integrated resource, applicable to all involved in work integrated learning, it will also appeal to pro-Vice Chancellors of teaching and learning, WIL coordinators, careers services, and all those involved with standards and competency.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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