Relationality in Indigenous Climate Change Education Research: A Learning Journey from Indigenous Communities in Bangladesh
Bibliographic record
Abstract
Abstract This article explores my relational learning reflections with the Laitu Khyeng Indigenous community in the Chittagong Hill Tracts (CHT), Bangladesh, focusing on Indigenous perspectives on climate change education. Implementing a relational theoretical framework, I share my reflections on relational learning in this research as part of being accountable to the Indigenous community. Through exploring Indigenous land-based climate change research, five central themes emerge Indigenous land rights, relationship with the environment, community-led relationality as collaboration, intergenerational relational knowledge and relationality as ethical reciprocity. The findings explore the intrinsic connection between Indigenous communities and their ancestral territories, emphasising the significance of upholding Indigenous sovereignty over land for sustainable adaptation to climate change. In this article, I highlight the importance of relational learning as a form of education, fostering resilience rooted in preserving traditional practices and spaces. Relationality with the environment is central to Indigenous climate education, promoting understanding and reciprocity with the land. In my learning, I learned that community dynamics and collaborative learning are essential for effective climate education, emphasising collective action and diverse perspectives. In relational learning, inter-generational knowledge transmission ensures the preservation and sharing of traditional land-based knowledge across generations, forming the foundation for sustainable adaptation strategies. Ethical engagement and reciprocity guide research interactions, emphasising mutual respect and cultural sensitivity. By centring Indigenous perspectives and knowledge systems, this study advocates for community-led approaches to climate change education, fostering resilience and environmental stewardship within Indigenous communities.
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.023 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 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".