From Environmental Degradation to Social Transformation: Exploring the Role of Eco-Justice in the Struggles of Indigenous Communities
Bibliographic record
Abstract
Eco-justice offers a vital framework for examining the intersection of environmental degradation, Indigenous rights, and environmental sociology. Indigenous communities worldwide continue to bear the disproportionate impacts of deforestation, mining, and pollution—harms that not only threaten their physical well-being but also sever deep-rooted cultural, spiritual, and ecological ties to their lands. This paper explores how eco-justice, through its core principles of distributive, procedural, and recognition justice, provides pathways to redress these injustices by advocating for equitable environmental burdens and inclusive decision-making that honors Indigenous sovereignty and knowledge systems. Drawing on case studies from the Amazon, Standing Rock, and the Canadian tar sands, the study highlights how Indigenous movements operationalize eco-justice in their resistance to resource extraction, and in their pursuit of land, cultural preservation, and autonomy. Central to this analysis is the role of Traditional Ecological Knowledge (TEK) in promoting sustainability and ecological resilience. The paper further addresses systemic challenges such as structural racism, tokenistic inclusion, and the exacerbating effects of climate change. It concludes with policy recommendations to integrate Indigenous perspectives into environmental governance and calls for future research that deepens the discourse on eco-justice in Indigenous environmental capaigns.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.079 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".