Enriching the intersection of service and transformative learning with Freirean ideas: The case of a critical experiential learning programme in Brazil
Bibliographic record
Abstract
In this article, we examine the value of combining transformative and service learning pedagogical practices in management education programmes to encourage management students to be more critical and reflexive regarding serious contemporary issues like social inequality and sustainability. We draw on a long-term management education experience conducted in the northeastern region of Brazil, where international students learn how to develop a real-time community-based project with local inhabitants. We argue that while service learning approaches promote pragmatic action-based principles, transformative learning acts at the epistemic level, contributing to change in values. In addition, Paulo Freire’s ideas are integrated to reinforce critical and reflexive dimensions of the learning experience. Our results offer a process-based model showing how a critical experiential learning pedagogy might lead to the development of community-based competences, which, in turn, might lead to changes in the deeply held values of the participants. Freire’s emancipatory ideas are applied not only regarding the relationship between teachers and students, but also to the distinction between Western and non-Western societies, going beyond questioning of the destructive consequences of financial capitalism to question the hegemony of one worldview over all other possible ones.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".