Assessment of Socioeconomic Factors and Stakeholders Involved in Dzanga Sangha Complex Protected Area, Central African Republic
Bibliographic record
Abstract
Dzanga Ndoki is the main National Park located in Dzanga Sangha Complex Protected Area, Central African Republic. This study assesses socioeconomic factors and different NGOs involved in management of the park. A questionnaire survey was used to collect data in 8 villages around the park. Results on socioeconomic study showed that younger people aged of 20-25 (36.55%) and between 25-30 (27. 81%) are more dynamic in the forest than elderly ones (age >35) who represented (5.61%). Better education may help in conservation of the Dzanga Ndoki due to different employment. Admittedly, 39.27% of people had primary education, (6.4%) had secondary level, (1.33%) had higher education and 53.18% were illiterate. Employment and access to market are missing. Participative management has mostly focused on villages of Mossapoula and Yandoumbe. Ba Aka people (60.52%) were not satisfied with the project. In addition, poor conditions of local people let them very dependent to forest resources (illegal hunting and gathering). This project is however unable to provide financial support to national NGOs and associations for local people.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".