Customary Institutions and Rules underlying Conservation Functions of Sacred Sites or Indigenous and Community Conserved Areas
Bibliographic record
Abstract
Sacred sites, or indigenous and community conserved areas (ICCAs), are repositories of biological and cultural diversity, the spaces de facto governed by Indigenous peoples or local communities. There are many thousands of these sites across the world, including sacred forests, wetlands, landscapes, village lakes, catchment forests, river and coastal stretches and marine areas. Though the backbone of sacred sites or ICCAs is the robust local governance system of Indigenous/customary institutions and their customary laws/rules, aspects such as institutional analysis, institutional governance, customary laws/rules and management systems are inadequately investigated. This article suggests how customary institutions or rules enable the underlying conservation functions of sacred sites or ICCAs and that due recognition and attention need to be given to indigenous protocols re ICCAs to enable the conservation of biological and cultural diversity. Through enabling legislation or policy, the customary institutions of traditional communities managing the sacred sites can be reinforced and restored. Relevance of sacred sites or ICCAs can be established in biodiversity conservation processes if the resilience of customary institutions and the ability of institutions withstanding external challenges are appreciated.
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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 | 0.005 | 0.009 |
| 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 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".