Researching the recognition of prior learning : international perspectives
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)
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".