Prior learning assessment and recognition: Emergence of a Canadian community of scholars
Bibliographic record
Abstract
Prior learning assessment and recognition (PLAR) is the practice of reviewing, evaluating, and acknowledging the information, skills, and understanding that adult learners have gained through experiential or self-directed (informal) learning rather than through formal education (Thomas, 2000). As our current economy and workplaces experience rapid and continuing change, PLAR offers a vital contribution to supporting lifelong and life-wide learning (Evans, 2000). Beyond significant benefits to individual adult learners in terms of confidence-building and enhanced reflective capacity, PLAR’s process translates personal and workplace learning into a portable format, a common coin suitable for public recognition in many different venues. PLAR has hence become an integral feature of lifelong learning policies around the globe and is closely linked with the implementation of national and transnational qualification frameworks (Morrissey et al., 2008). PLAR scholars have a vital role in ensuring that policy and practice in this important field is informed by innovative research. This brief report describes a workshop on scholarly PLAR research, held in Ottawa, Canada on November 6 and 7, 2010 with funding from the Social Sciences and Humanities Research Council (SSHRC).
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.
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 | 0.055 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.026 | 0.039 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".