Participation, Learning and Environmental Justice: A Case Study of Protected Area Planning and Management in the Kullu District of Himachal Pradesh, India
Bibliographic record
Abstract
Abstract Achieving environmental justice in protected area (PA) planning and management has been historically problematic. Herein, potential connections between learning outcomes acquired through PAs and advancements in environmental justice are examined and assessed through a case study of PAs in the Kullu District of Himachal Pradesh, India. Specifically, our study aimed to identify learning outcomes that contributed to positive changes in distributive, procedural, recognitional and restorative justice for local people managing or residing near PAs. As throughout the Himalayas, the land use rights, both customary or recognised by law, of local inhabitants in the Kullu District have been altered and eroded through the establishment of PAs, which has resulted in poor environmental justice outcomes. Interviews were conducted with local people living near PAs, forest officers working in PAs, relevant government officials, academics, and NGO representatives. The results indicate that non-formal and informal learning has produced positive cognitive and relational changes in local inhabitants as well as forest officers, which has led to modification of policies, positive environmental change, and enhanced aspects of environmental justice. Though positive changes emerged, the study also identified a need for increased learning opportunities, particularly for inhabitants of more remote areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".