Forest dynamics and local perceptions of conservation: the case of Bontioli forest in south-western Burkina Faso
Bibliographic record
Abstract
Introduction The widespread decline of forests across many countries has prompted governments to implement various measures aimed at conserving forest resources. In the Bontioli Total and Partial Wildlife Reserves, however, effective forest conservation remains a challenge, despite the state’s promotion of participatory management approaches. Methods This study employed a diachronic analysis of satellite imagery spanning from 1991 to 2024 to assess land use dynamics, complemented by a qualitative survey involving 150 participants, comprising 26 institutional stakeholders and 124 community members, selected through a combination of random and purposive sampling across four villages. Data were collected through semi-structured interviews and focus group discussions and analyzed using mixed thematic coding (both inductive and deductive) with NVivo 14 software. Results Spatial analysis revealed a steady pattern of deforestation, marked by significant agricultural expansion and a reduction in natural vegetation, primarily driven by human activities. Stakeholder perceptions indicated broad awareness of deforestation and its ecological consequences. However, the study also highlighted notable divergences between local communities and authorities in their understanding of concepts such as deforestation and conservation. While community members acknowledged their contribution to environmental degradation, they largely attributed responsibility to external actors and the weakening of traditional authority structures. They also expressed a need for greater institutional recognition and support to align conservation efforts with local development priorities. Discussion The study concludes by recommending inclusive governance models grounded in sociocultural values, along with the development of economically viable alternatives to foster stronger community participation in conservation. Nevertheless, as these findings pertain specifically to the Bontioli forest, further research should be conducted in other classified forest areas to support broader and more comparative analyses.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".