Biodiversity conservation: Officials' perceptions and discord with community forest management in Nepal
Bibliographic record
Abstract
Two topics receiving much attention in design of forest policy and management in Nepal are conservation of biodiversity and participation of forest-local people. Government officials, forest users and development workers are all involved in shaping policy for the management of forest for biodiversity and other values. It is therefore crucial to understand the different viewpoints about biodiversity among these stakeholders. This paper is derived from a broad case study on the views of various stakeholders in community forestry in Nepal, but is focused on understanding the views of policy-level government officials in regards to biodiversity conservation. Using a grounded theory approach, qualitative data were collected on two field visits in 2002–2003 to the study area. The results of interviews with officials indicate diverse perspectives in interpreting biodiversity conservation. These include perceptions of forest users' understanding about diversity, and strong beliefs about definition of biodiversity and about dependence of users on forest for their livelihood. Implications of the results include an obvious need for better understanding by staff at various levels of government and other agencies involved in community forestry, of the different concepts and views held about biodiversity conservation. A broader understanding among officials of biodiversity and deeper knowledge of other's views on biodiversity conservation could help in designing and implementing policies and programs in the context of community forest management. Key words: views, perceptions, understanding, community, forestry, users, government officials, policy, qualitative method
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".