Bibliographic record
Abstract
This article argues that labor market demand is an insufficient—and unstable—justification for expanding Prior Learning Assessment and Recognition (PLAR) for newcomers seeking entry into regulated professions. Drawing on PLAR’s “credit exchange” and “developmental” models and a Kantian ethical frame, it advances a normative claim that PLAR should be designed to benefit the person being assessed and that benefits to institutions, employers, or society must not come at the assessed individual’s expense. Using examples from Canadian provincial credential-recognition reforms, the paper shows how equity gains often occur only when shortages create political pressure, leaving newcomers vulnerable to renewed gatekeeping once demand shifts. The article concludes that PLAR policy and practice should be grounded explicitly in ethical commitments to fairness and non-harm, rather than relying primarily on human-capital or workforce rationales.
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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".