Bibliographic record
Abstract
Introduction by Bonnie Kennedy , Executive Director Canadian Association for Prior Learning Assessment (CAPLA) The Canadian Association for Prior Learning Assessment (CAPLA) has been particularly busy this year! They have expanded their http://capla.ca/ website to include a special online resource called Experience Matters . The information is intended to acquaint immigrants, pre- and post-arrival, with a range of learning assessment and recognition processes they may encounter. The three areas include formal credential assessment, language assessment and the assessment of knowledge, skills and abilities (PLAR/RPL). CAPLA also launched a new publication titled Quality Assurance for the Recognition of Prior Learning (RPL) in Canada – THE MANUAL. Among other things, it contains new RPL guiding principles and self-audit checklists that enable users to evaluate their RPL practice. Work on the project began in 2013 and involved multi-stakeholder engagement, along with collaboration among many national organizations and all of Canada’s provinces and territories. It was funded by the Government of Canada and was rolled out at CAPLA’s yearly conference in Toronto on October 21-23, 2015. Copies can be purchased online at http://capla.ca/rpl-qa-manual/ . Note Thank you to Bonnie Kennedy and CAPLA for allowing us to share these resources with our PLAIO 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.666 | 0.487 |
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".