Indigenous women-led climate crisis solutions: A decolonial perspective from the Garo Indigenous community in Bangladesh
Bibliographic record
Abstract
This paper explores the critical impact of climate change on land-based culture and matriarchy within the Garo Indigenous Community in Bangladesh. Using a decolonial perspective, we explored how Garo Indigenous women are deeply rooted in land-based traditions and social structures and face unprecedented challenges. Through a decolonial lens, we explore intersections between climate change, land-based practices, and the matriarchal land-based practice. It shows the Garo Indigenous community's land-based adaptive strategies and resilience in climate change. This paper emphasizes the importance of centering Indigenous perspectives in climate discourse, advocating for decolonization as a crucial framework for understanding and addressing the multifaceted impacts of climate change on land-based cultures and matriarchy. By providing decolonial analysis from the Garo Indigenous land-based perspective, this research contributes to a broader understanding of the effects of climate change on Indigenous communities, creating a decolonized and traditional land-based approaches to climate adaptation and mitigation policies. The paper calls for action to recognize Indigenous land-right, traditional matriarchy family leadership to safeguard the unique cultural heritage and gender dynamics of the Garo Indigenous community while addressing the broader implications for global climate justice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".