Handbook of the Recognition of Prior Learning
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 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.
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 | 0.024 | 0.059 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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