Children as Agents of Change: Research Reflections on the Practice and Possibilities of Participatory Eco-justice Research
Bibliographic record
Abstract
This article takes the form of a conversational text in which we as five adult educational researchers reflect on our varied and intersecting experiences in conducting eco-justice education research with elementary school students as co-researchers. Drawing on a theoretical and rhizomatic foundation of children’s rights, sociology of childhood, and eco-justice, we describe our engagement with a Grade 5/6 class on a participatory research project centered on vermicomposting. We situate ourselves within the action research spiral of ‘plan-act-reflect’ as we dialogue and think together about our experiences as co-researchers with students and look ahead to future opportunities to fully realize children’s rights and justice in environmental education and research. We center our discussion on three guiding questions: 1) What is eco-justice education and what might it look like within the context of participatory research with children? 2) How can adult researchers empower children’s autonomy and active citizenship in ecological research while engaging as co-researchers with them? 3) What are the strengths, challenges, and possibilities for children’s participation as eco-justice and climate action researchers? Our reflective text contributes to a body of knowledge around the meaningful engagement and empowerment of children in eco-justice and climate action explorations and activism.
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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 | 0.061 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.026 | 0.109 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".