Bibliographic record
Abstract
The Handbook of the Recognition of Prior Learning: Research into Practice (2014: NIACE) is now available for free download at http://www.learningandwork.org.uk/our-resources/downloadable-publications/handbook-recognition-prior-learning . From the original information sheet about the handbook: handbook, organised thematically, consolidates the major research findings of experienced RPL researchers from around the world, identifying future research directions and drawing together evidence-based implications for policy and practice. It is an extremely useful and timely resource as recognition of prior learning continues to develop around the world, especially in the context of lifelong learning policy and national and regional qualifications frameworks. This internationally relevant text will be of particular interest to: academics, researchers and students in education, policy studies, widening participation, lifelong, adult, continuing, recurrent and initial education and learning practitioners concerned with RPL policy and actual RPL practice who wish to understand research and its relationship to practice or wanting to research (and improve) their own practice human resources managers. PLAIO would like to thanks NIACE and the co-editors of the book -- Judy Harris, Christine Wihak and Joy Van Kleef -- for allowing us to share this resource with our readers.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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".