Efficacy of incentives in eliciting the people's participation in conservation of common property resources in different forest divisions of Himachal Pradesh, India
Bibliographic record
Abstract
Common property resources (CPRs) in Himachal Pradesh include forests, village common lands, ponds, Bawaries, threshing grounds, irrigation water channels, river banks, temples, paths etc. The people's participation in conservation and maintenance of these resources was achieved through the formation of JFMCs/VFCs under National Afforestation Programme (NAP). In lieu of the participation various incentives were given to different JFMCs in the state for the protection and conservation of these resources. Due to these incentives the maximum participation level was found in between 20–30, 30–40, 20–30 and 30–40 per cent in JFMCs of Nalagarh, Dharamshala, Dalhousie and Jogindernagar forest divisions respectively. Overall maximum level of participation in all the forest divisions was found in between 20–30 per cent followed by 30–40 per cent. None of JFMCs in different Forest divisions observed 50 per cent or more level of participation. For future planning and replication of such projects more and more group discussions, meetings, awareness camps etc should be conducted. Proper usufruct sharing mechanism and conflict resolution must be addressed properly. Some more incentives in consultation with the stakeholders must be given so that they can participate without any hesitation.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".