Many mountain paths: Perceiving change in the management of community forests in the Hindu Kush Himalaya, Uttarakhand, India
Bibliographic record
Abstract
India's high mountain, van panchayat forests are a long-standing example of community-led forest governance. These provide vital support for mountain communities’ wellbeing, yet little is known about how environmental and social change is currently perceived or addressed, despite many claims that characterize van panchayats to be in crisis due to policy interventions by state forest actors and erosion of local management institutions. This research investigates how changes in forest dependence, governance, and health drive forest management decisions. Using open-ended interviews with 41 forest stewards and knowledge holders in a high mountain valley in Uttarakhand, our analysis describes managers’ perceptions of environmental and social changes and their implications for management choices. We found that van panchayats are currently navigating stewardship pathways in response to: (a) population shifts and changes to historically forest-dependent livelihoods; (b) a shift in governance regimes across van panchayats, affected by both regulatory changes and local institutional capacity; (c) the effects of global environmental change as it intersects with local influences on mountain ecosystems. Forest stewards’ perceptions of the drivers, trends, significance, and appropriate management responses to these changes varied widely. An overall decline in rightsholders’ dependence on forest resources was commonly reported, as was improved forest health in van panchayats in relation to other forest types. However, van panchayat managers disagreed on whether declining dependence positively or negatively affects forest health, whether the state is absent or actively wresting control from communities, and if successful forest restoration efforts will continue. A lack of shared understanding of these issues complicates forest stewards’ efforts to cooperate towards mutually desired ends, exacerbated by a co-management policy which isolates forest councils’ efforts from neighbouring forests. Accordingly, we encourage policy changes to enhance collaborative decision-making and to address implications of diverse perceptions, priorities and practices of care in mountain forests.
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 | 0.001 | 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 | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".