Bibliographic record
Abstract
This paper explores the issues involved in granting college and university credits for prior learning, particularly informal workplace learning and workplace training. It argues for the recognition of the differences (but not the superiority of one over the other) between workbased knowledge and academic knowledge when granting recognition of prior learning. It criticizes exaggerated claims for, and processes used in the recognition of prior learning but defends a role for judicious use of prior learning assessment and recognition (PLAR) within the academy. It further argues that traditional institutions of higher learning do need to change to accommodate adults within the academy and that PLAR has a role to play in that process. Résumé Cet article explore les questions entourant les crédits universitaires et collégiaux associés à la reconnaissance des acquis, particulièrement à l'apprentissage informel et la formation en milieu de travail. Il veut démontrer les différences (en non la supériorité de l'un sur l'autre) entre le savoir acquis au travail et le savoir universitaire dans la reconnaissance des acquis. Il critique les prétentions excessives et les processus utilisés en reconnaissances des acquis, mais défend l'utilisation judicieuse de l'ÉRA (Évaluation et reconnaissance des acquis) par les institutions postsecondaires. Il va plus loin en soutenant que les institutions de haul savoir devaient changer leurs exigences d'admission pour permettre aux adultes d'avoir accès à leurs programme et que l'ÉRA devait avoir un rôle à jouer.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".