Indigenous Land-Based Perspectives on Environmental Sustainability: Learning from the Khasis Indigenous Community in Bangladesh
Bibliographic record
Abstract
This research explores Indigenous land-based perspectives on environmental sustainability, centering on the Khasis Indigenous community in Bangladesh. With a critical connection to their land-based cultural heritage and environment, the Khasis community offers a distinctive perspective for examining environmental challenges. Emphasizing the traditional land-based knowledge and practices of the Khasis, as well as their insights on environmental challenges, this study employs a land-based theoretical framework. It sheds light on the adaptive strategies of Khasis Indigenous communities amidst shifting environmental conditions, illustrating how they integrate sustainable practices into daily life by drawing upon Indigenous land-based knowledge and practices to mitigate environmental risks. Additionally, the research scrutinizes the challenges faced by the Khasis in preserving their land-based knowledge, practices, and identity in the face of a global environmental crisis. The findings contribute to broader discussions on Indigenous land-based knowledge and practices and their significance in contemporary climate discourse. By amplifying the voices of the Khasis Indigenous community’s land-based knowledge and practices, this study advocates for the incorporation of Indigenous perspectives into climate policies and interventions. It underscores the necessity for a more inclusive and culturally sensitive approach to environmental sustainability, one that acknowledges and respects the resilience of Indigenous communities like the Khasis in Bangladesh.
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.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".