Intersections between Participatory Evaluation and Social Pedagogy When Assessing Socio-Educational Projects
Bibliographic record
Abstract
Social pedagogy is a science, a practice, and an art that was born from the need to provide an educational response to sociocultural situations and problems that confront individuals, groups, and communities. Participatory evaluation is a methodological strategy in which program evaluation is understood through the construction of shared knowledge, user participation, and collective decision-making. Although both social pedagogy and participatory evaluation have been variously defined in academic debates, an analysis of the intersection between the two is lacking. The main objective of this study is therefore to analyze the relationship and intersections between participatory approaches in evaluation and the scientific discipline of social pedagogy. To this end, a semi-structured questionnaire was designed to collect quantitative and qualitative data from 18 academics in seven countries. The results reveal the importance of eight pedagogical dimensions identified in the development of participatory evaluation, which are defined in this research. The study also discusses the potential and limitations of applying participatory evaluation to socio-educational projects. The aim of this research is to broaden the scientific debate on the intersection between social pedagogy and participatory evaluation, providing data that will improve the implementation of participatory evaluation in social pedagogy, thus encouraging greater user participation in this context.
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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 | 0.352 | 0.389 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".