Researching the recognition of prior learning : international perspectives
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
Foreword (Judith Murray) Information about the authors 1. Introduction and overview of chapters (Judy Harris and Christine Wihak) 2. Australia: An overview of 20 years of RPL research (Roslyn Cameron) 3. Canada: A typology of PLAR research in context (Joy Van Kleef) 4. Quebec: An overview of RAC/RPLC research since 2002 (Rachel Belisle) 5. England: APEL research in higher education (Helen Pokorny) 6. European Union: VNFIL research and system building (Judy Harris) 7. Research reveals 'islands of good practice' in Organisation for Economic Co-operation and Development (OECD) countries (Patrick Werquin and Christine Wihak) 8. Scotland: RPL research within a national credit and qualifications framework (Ruth Whittaker) 9. South Africa: Research reflecting critically on RPL research and practice (Mignonne Breier) 10. Sweden: The developing field of validation research (Per Andersson and Andreas Fejes) 11. United States of America: PLA research in colleges and universities (Nan Travers) 12. PLAR and the teaching-research nexus in universities (Angelina Wong) 13. Research into PLAR in university adult education programmes in Canada (Christine Wihak and Angelina Wong) Endword: Reflections on research for an emergent field (Norm Friesen)
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.013 | 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