Protected Area Versus People Conflict and a Co-Management Programme: A Case Study from the Dhudpukuria-Dhopachari Wildlife Sanctuary, Bangladesh
Bibliographic record
Abstract
Conflicts over the conservation of natural resources at the community level occur in different forms and at various levels of severity. These conflicts can be defined as situations in which the allocation, management or use of natural resources results in attacks on human rights or denial of access to natural resources to an extent that considerably diminishes human welfare. However, the conflict between the authorities of the Dhudpukuria-Dhopachari Wildlife Sanctuary (DDWS) and local people over wildlife conservation is one of the most serious conservation issues in Chittagong region of Bangladesh. The DDWS is managed under a co-management programme, but there are many questions that have already been asked about the success of co-management in the study area. A total of 195 standardized, structured and semi-structured questionnaires were administered randomly to villagers. The majority of respondents reported that they did not receive any potential benefit from the DDWS, and almost one-third of respondents reported that they had problems with the DDWS. Almost all respondents reported that they were unable to control the damage caused by wildlife. More than 80% of respondents reported that the co-management approach was not effective in mitigating conflict between people and protected areas. More than 45% of the participants in co-management program reported greater effectiveness of the co-management approach than non-participants. Moreover, the respondents who received more benefits from the Protected Areas (PA) reported more effectiveness of the co-management approach than those who received less or no benefits from the protected area. Integration of local knowledge and preferences into the co-management process will ensure the sustainability of the co-management programme by minimizing the conflict between people and protected areas.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".