Recognizing Student Activism. Analysing Practices in Recognizing Informal Learning in the EHEA
Bibliographic record
Abstract
Abstract This paper aims to answer the question of how recognition of student engagement as informal learning takes place in HEIs within the EHEA. It identifies challenges, best practices, and lessons learned for the recognition of informal learning in the EHEA in general. Questions of transparency in recognition of informal learning in student activism, their legal basis and ways of implementation as well as student representatives’ experiences are discussed. Analysis was undertaken based on two surveys in the EHEA. The first survey addressed student representatives at national level in 11 countries, aiming for insights in legal conditions and practices of higher education institutions’ recognition of informal learning of student activists. The second survey focussed on student representatives at institutional level (80 respondents), sharing their experiences on formalities, barriers and practicalities within implemented policies of recognition of prior learning in student activism. Based on the collected data, findings and recommendations are presented in the last part of the paper.
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How this classification was reachedexpand
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".