Bibliographic record
Abstract
AIM: The aim of the study was to develop and promote national benchmarks for those engaged in accreditation of prior learning (APL) termed 'prior learning assessment and recognition' (PLAR) assessment in Canada, in all sectors and communities. The study objectives were to gain practitioner consensus on the development of benchmarks for APL (PLAR) across Canada; produce a guide to support the implementation of national benchmarks; make recommendations for the promotion of the national benchmarks; and distribute the guide. The study also investigated the feasibility of developing a system to confirm the competence of APL (PLAR) practitioners, based on nationally agreed benchmarks for practice. METHOD: A qualitative research strategy was developed, which used a benchmarking survey and focus groups as the primary research tools. These were applied to a purposive sample of APL practitioners (n = 91). The participants were identified through the use of an initial screening survey. RESULTS: Respondents indicated that in Canada, PLAR is used in a variety of ways to assist with individual and personal growth for human resource development, the preparation of professionals and the achievement of academic credit. The findings of the focus groups are summarised using a SWOT analysis CONCLUSION: The study identified that the main functions of the PLAR practitioners are to prepare individuals for assessment and conduct assessments. Although practitioners should be made aware of the potential conflicts in undertaking combined roles, they should be encouraged to develop confidence in both functions.
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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 | 0.070 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".