A Case Study on the Recognition of Prior Learning (RPL)
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 recognition of prior learning (RPL) has been implemented to varying degrees across Canada in secondary schools and post-secondary institutions, through workplace training models, businesses, sector councils and industry groups, apprenticeship, the military, and professional accrediting/regulatory bodies. RPL however remains fragmented and seriously under-supported at Canadian universities. As a key driver, RPL can play a leading role in addressing labour force changes, economic competitiveness, facilitating access to post-secondary education, and in the recognition of foreign credentials. While RPL challenges the university hierarchies of knowledge, learning and power, there are significant social and economic consequences for failing to address the increasing amount of unrecognized learning. This doctoral case study explored the general perceptions of RPL role-players at a western Canadian university during the summer of 2017. The results revealed there were lost opportunities and differences in understanding RPL. Additional findings relate to the invisibility of RPL, roadblocks to implementation, an intrinsic belief in the value and benefits of RPL, and some constructive ideas for moving forward. These findings point to directions for future research.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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