Los sistemas de reconocimiento y acreditación de los aprendizajes no formales e informales: referencias históricas, funciones socioeducativas y perspectiva teórica
Bibliographic record
Abstract
The recognition and accreditation of learning acquired in informal and non-formal contexts are not only becoming standard practice in developed countries, but they are also imposing changes on a great number of long-held beliefs and attitudes which are still present in the field of education today. From a conceptual point of view,we are referring to evaluating criteria consisting of the recognition and certification of the individual competencies that a person has, regardless of when, where and how they have been acquired. In practical terms, an expert group made up of professionals from the formal educational system will evaluate certain learning experiences that are equivalent to the competences of an official qualification.These competences could be acquired through formal, non-formal or informal learning. This paper shows some of the results of an investigation on Accreditation Systems. Firstly, we conduct a brief historical overview of these educational practices in USA, Canada and Europe, examining the current situation in Spain more closely. Secondly,we analyse the purposes and advantages that these accreditation systems bring to workers, enterprises, society and, generally, to the education system. And finally, some particular aspects are considered for further reflection, as we consider them to be the basis for a theoretical discussion about these new qualification systems: problems that arise from them, principles and features shared by most professionals, the theoretical framework from which they should be approached, and their inevitable connection with formal, non-formal and informal education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".