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Record W4413187217 · doi:10.3138/cjpe-2023-0049

Intersections between Participatory Evaluation and Social Pedagogy When Assessing Socio-Educational Projects

2025· article· en· W4413187217 on OpenAlexvenueno aff
Héctor Núñez López, Àngela Janer Hidalgo, Carolina Molina

Bibliographic record

VenueCanadian Journal of Program Evaluation · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsCitizen journalismSociologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.617
GPT teacher head0.632
Teacher spread0.015 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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