Decolonizing Climate Change Adaptations from Indigenous Perspectives: Learning Reflections from Munda Indigenous Communities, Coastal Areas in Bangladesh
Bibliographic record
Abstract
This study explores the imperative need for decolonizing climate change adaptation strategies by focusing on Indigenous knowledge and perspectives. Focusing on the Munda Indigenous communities residing in the coastal areas of Bangladesh, the research offers critical insights into the intricate relationship between Indigenous wisdom and sustainable climate adaptation. By engaging with the Munda Indigenous people and their traditions, this study explores how traditional ecological knowledge and practices can inform and enhance contemporary climate adaptation efforts. Following the decolonial theoretical research framework, this research used participatory research methods and collaboration with the Munda Indigenous community. In this study, we shared our learning reflections to uncover unique approaches to climate resilience, including traditional community-based disaster risk reduction and cultural practices that foster social cohesion. These insights challenge the prevailing Western-centric climate adaptation paradigms, emphasizing recognizing and valuing Indigenous voices in climate discourse. The research underscores the significance of empowering Indigenous communities as key stakeholders in climate adaptation policy and decision-making. It calls for shifting from top-down, colonial approaches towards more inclusive, culturally sensitive strategies. The Munda Indigenous communities’ experiences offer valuable lessons that can inform broader efforts to address climate change, fostering resilience and harmonious coexistence between people and their environment. This study advocates for integrating Indigenous knowledge, practices, and worldviews into climate adaptation frameworks to create more effective, equitable, and sustainable solutions for the challenges posed by climate change.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| 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".