Ecopolitics, Extractivism and Indigenous People: Development Paradox of the Asur Tribe in Jharkhand, India
Bibliographic record
Abstract
Ecopolitics of extractivism in natural resource-rich regions and the struggles of Indigenous communities are globally known phenomena. This article outlines the ecopolitics of extractivism leading to a development paradox, while the Indigenous Asur communities encounter the detrimental effects of mining and unfulfilled pledges of developmental agendas. Based on a qualitative research approach, it explores the lived experiences of the Asur (a Particularly Vulnerable Tribal Group) in Gumla district of Jharkhand in India. It reveals that the Asur tribal people, influenced by promises of socio-economic development and better employment opportunities, consented to the acquisition of their land for bauxite mining. However, the failure of private sector entities to fulfil these promises has contributed to the policy-enabled patterns of mineral resource exploitation. The socio-ecological compensation for the sacred Asur tribe land formed a paradoxical scenario and development dynamics. The article underscores that the preservation of land rights, environmental sustainability and social welfare for tribal people has notably been futile in India. Moreover, the extractivism leading to severe health hazards, ecological destruction, depletion of forest resources, environmental pollution and loss of livelihood within the Asur habitat evidenced from the narratives. The analysis has implications for rights-based, ecologically grounded and Indigenous community-led approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".