Using community-based participatory action research to create culturally grounded education at scale: a study of systems change in Peru
Bibliographic record
Abstract
In this article, we share the process of a long-term community-based participatory action research (CBPAR) project working with Quechua communities in Peru to co-create culturally grounded curriculum materials. We show how policy advocacy, collaboration between Indigenous communities, governments, and social organizations can work to systematically address long-standing social justice issues in education. The project, which has been running since 2016, uses an iterative approach to collaboratively develop quality, culturally grounded educational materials that honors students’ and parents’ lands, identities, cultures, values, needs, and goals. To make these processes scalable, we demonstrate how to identify policy windows and levers for change to bring the knowledge and voices of community members into curriculum content creation, which is historically reserved for people unfamiliar with community realities. We use a multi-pronged, multi-theory, multi-epistemological approach to discuss the practices and considerations we used and learned for forging effective collaborations between community members, educational specialists, and CBPAR researchers, as well as the tensions and issues that have arisen during the project.
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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 | 0.037 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".