Drivers of Biodiversity Conservation in Sacred Groves: A Comparative Study of Three Sacred Groves in Southwest Nigeria
Bibliographic record
Abstract
Globally, sacred groves represent a traditional form of community-based conservation, recognized as areas of cultural and religious importance to local people. In some cases, the entire community guards against the desecration of, or unauthorized access to, such sites, either by its members or outsiders; in others, non-recognition of customary rights is linked to degradation. This paper uses the case study of three sacred groves in southwest Nigeria to examine the extent to which perceived socio-economic and religio-cultural benefits contribute to biodiversity conservation in sacred groves with different scales of governance. Using mixed methods approaches, we found that the long-term preservation of sacred groves and their biodiversity depend on collaboration between: i.) customary institutions (community-based conservation through a system of established traditional norms and prohibitions), and ii.) formal government legislation and management. The recognition of sacred groves as national monuments and UNESCO World Heritage Site has paved the way for biodiversity protection, increasing cultural tourism, socio-economic rewards and the preservation of religio-cultural values. We present local peoples’ assessments of the benefits of sacred groves and offer suggestions to improve community engagement and protect the biodiversity within sacred groves in Nigeria.
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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 | 0.000 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".