Introduction - Environmental conflicts and community management of natural protected areas in French-speaking Africa
Bibliographic record
Abstract
In most French-speaking countries of sub-Saharan Africa, environmental conservation areas are largely the legacy of colonial occupation. For more recent protected areas, their conceptualization, establishment and management programs are almost exclusively carried out by international players, in more or less close collaboration with national authorities, and with varying degrees of consultation and integration of local communities living in or near these environmental spaces. Presented as international development projects, these protected areas are for the most part created ex nihilo, and are only weakly integrated into the territories and their socio-environmental, political and economic structures. This introduction to the thematic issue provides a non-exhaustive synthesis of the literature on protected areas, environmental conflicts and the prospects for community integration and management by and for riparian communities. It examines how, on the one hand, the endogenous environmental, social and political norms and practices of riparian communities can contribute to the conservation of biodiversity and, on the other, how their integration into the management processes of natural protected areas can help strengthen environmental conservation programs and the appropriation of the environmental services they can provide.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".