Relationality in Indigenous Climate Change Education Research: A Learning Journey from Indigenous Communities in Bangladesh
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it