Towards a critical service learning in geography education: exploring challenges and possibilities through testimonio
Bibliographic record
Abstract
There has been an increasing interest in exploring the transformational possibilities of experiential learning approaches like service learning, across post-secondary education, including geography. At the same time, scholars caution that such initiatives can entrench neoliberalism, white supremacy and other power structures and call for implementing a critical service learning (CSL) approach that is rooted in action against injustice. In response, this paper uses testimonio methodology to explore the experiences of a student and instructor engaging in a graduate geography course that implements CSL. We demonstrate how CSL is a complex process that is mired in the very power structures and institutional barriers it attempts to disrupt. Nonetheless, CSL creates opportunities for social change in the classroom and community, which make it a promising pedagogical strategy for geographers aiming to create alternative teaching approaches in their classrooms.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.032 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".