How Van Panchayat Rule Systems and Resource Use Influence People’s Participation in Forest Commons in the Indian Himalayas
Bibliographic record
Abstract
Van Panchayats (VPs) are self-initiated forest management groups institutionalized since 1931 in the Himalayan Uttarakhand state of North India. VPs are considered to be successful case of Community-Based Forest Management (CBFM) despite an observed decline in VP practice around the 1990s. This study clarifies CBFM in the context of local rules, forest resource use and people’s participation. It reveals the possible factors behind better resource management of forest commons use from four VPs in Uttarakhand. A multi-dimensional research approach was followed comprising a literature review of the state forest department data, forest inventory, interviews with village leaders as snowball samplings in several villages, and semi-structured interviews with villagers/house-holders. Results showed that local rules are different depending on the villages expect for prohibited timber logging. The most useful tree species for local people was Banji oak (Quercus leucotrichophora) and every village had an oak forest which was utilized for fuelwood and fodder for daily livelihoods. VP forest size and the basal area of trees also influenced people’s participation in forest management. Much larger size of the VP forest land is declining due to the people’s de-motivation for forest management. Furthermore, a higher education of the householder increased the level of participation. Transparency of Management Committee (MC) for the VP members is an important aspect. To summarize, availability and utilization of the valuable forest resources and its management by villagers following local rules and the VP system was considered to influence people’s participation in the forest commons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".