Predicting Attitudes towards Protected Area Management in a Developing Country Context
Bibliographic record
Abstract
Biodiversity conservation through use of protected areas relies significantly on the attitudes of local adjacent communities. Some studies suggest that attitudes are often shaped by the associated positive and negative externalities and socio-demographic and economic characteristics of local communities living adjacent to protected areas. The current study sought to identify useful predictors of local attitudes towards protected area management. It was conducted at Bwindi Impenetrable National Park in Uganda where several interventions in form of benefits to improve local people’s attitudes towards the park have been implemented for the last 30 years. The study examined the extent to which these benefits can influence local people’s attitude towards management of the Protected Area (PA). A household survey was conducted among 190 randomly selected respondents and Generalised Linear Mixed Models (GLMMs) fitted where the dependent variable was a binary “Good” or “otherwise” response to how the respondent considered own relationship with park management. Socio-economic attributes of the respondents were used as control variables. The importance of cost variables (e.g. crop raiding) was also examined. The study found that only direct and material benefits were consistent predictors of a positive attitude towards management. Non-material and indirect benefits as well as the socio-economic factors and costs did not influence the attitude of local communities towards management. It can be concluded that positive attitude towards protected area management is determined by access to direct and material benefits by local communities and not socio-economic factors or costs incurred. Interventions intended to influence local communities to have a positive attitude towards management ought to emphasize direct and material benefits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".