Bibliographic record
Abstract
The term Recognition of Non-formal and Informal Learning Outcomes is preferred to all the other existing ones, such as Recognition of Prior Learning (RPL), for reasons that will become clear all along this chapter. PERSPECTIVES ON SKILLSit would be rather unfair to award qualifications on the sole basis of a duration of learning: learning could take place without learning outcomes being developed and policy makers should be aware of this.Other terms, such as Prior Learning Assessment and Recognition (PLAR, only in Canada), Accreditation of Prior Learning (APL, mainly in the UK), Validation of Experiential Learning Outcomes (VAE 9 , only in France and some French-speaking countries mimicking the French rather pioneering approach) and Recognition of Learning Outcomes (RLO, European Commission) all have advantages, but also several drawbacks.In short, most of these terms do not signal whether a) what is potentially validated and then recognised as the outcomes of learning, not the learning itself and b) what matters is the part of recognition. Key ChallengesNon-formal and informal learning -and its sibling concepts such as formal learningare not new concepts and have over the last decades received considerable attention.This trend has been reinforced, in particular, by the need for measuring participation in education and for classifying educational/learning activities, and by the development of approaches that confer currency -in the labour market and in the formal education and training system -to all learning outcomes regardless of the learning context which might be formal or not.The possibility of competences that
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".