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Record W2914937823 · doi:10.33002/nr2581.6853.01021

Customary Institutions and Rules underlying Conservation Functions of Sacred Sites or Indigenous and Community Conserved Areas

2018· article· en· W2914937823 on OpenAlexaff
Hasrat Arjjumend, Henrie Beaulieu-Boon

Bibliographic record

VenueGrassroots Journal of Natural Resources · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of LethbridgeMcGill University
Fundersnot available
KeywordsIndigenousCorporate governanceGeographyEnvironmental resource managementBiodiversityLegislationBiodiversity conservationDiversity (politics)Environmental planningPolitical scienceEcologyLawBusinessBiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.272
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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