Prior learning assessment and recognition (PLAR) and the impact of globalization : a Canadian case study
Bibliographic record
Abstract
This is a case study of participant narratives in the prior learning assessment and recognition (PLAR) process at The Royal Military College (RMC) in Kingston, Ontario. RMC is under the direction of the Department of National Defence Canada, and has a full-time staff dedicated to the maintenance and expansion of the prior learning assessment and recognition process. The research problem of this thesis is to ask if a participant in a prior learning assessment and recognition program acknowledges their own knowledge as valuable and how this may be linked to the motivating factors that cause the participant to return to formal study. In order to provide a context for the research problem, I use the literature review to examine the history of prior learning assessment and recognition programs in Canada. Through a mail survey, the use of participant narratives provides a voice to the literature in discussing the question of how knowledge is valued in a prior learning assessment program. Although the focus of the thesis will be prior learning assessment and recognition in Canada, the background to the concept of prior learning assessment is found in the United States. It is therefore essential to begin there in order to provide a context in which to understand the current Canadian situation. As a timeline the study begins with the post-World War II era and continues to the present. The sub-theme of my doctoral research is to investigate how the concept of prior learning assessment and recognition in Canada has been influenced by the broader context of globalization.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.037 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".