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Record W4361867637 · doi:10.15451/ec2018-08-7.12-1-26

Wildlife and spiritual knowledge at the edge of protected areas: raising another voice in conservation

2018· article· W4361867637 on OpenAlexaff
Sarah Bortolamiol, Sabrina Krief, Colin A. Chapman, Andrew Seguya, Marianne Cohen

Bibliographic record

VenueEthnobiology and Conservation · 2018
Typearticle
Language
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsMcGill University
FundersUniversité Paris Diderot
KeywordsWildlifeRaising (metalworking)Wildlife conservationEnvironmental resource managementWildlife managementEnvironmental planningGeographyEnvironmental scienceEcologyEngineeringBiology

Abstract

fetched live from OpenAlex

International guidelines recommend the integration of local communities within protected areas management as a means to improve conservation efforts. However, local management plans rarely consider communities knowledge about wildlife and their traditions to promote biodiversity conservation. In the Sebitoli area of Kibale National Park, Uganda, the contact of local communities with wildlife has been strictly limited at least since the establishment of the park in 1993. The park has not develop programs, outside of touristic sites, to promote local traditions, knowledge, and beliefs in order to link neighboring community members to nature. To investigate such links, we used a combination of semi­directed interviews and participative observations (N= 31) with three communities. While human and wildlife territories are legally disjointed, results show that traditional wildlife and spiritual related knowledge trespasses them and the contact with nature is maintained though practice, culture, and imagination. More than 66% of the people we interviewed have wild animals as totems, and continue to use plants to medicate, cook, or build. Five spirits structure human­wildlife relationships at specific sacred sites. However, this knowledge varies as a function of the location of local communities and the sacred sites. A better integration of local wildlife­friendly knowledge into management plans may revive communities’ connectedness to nature, motivate conservation behaviors, and promote biodiversity conservation.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.029
GPT teacher head0.246
Teacher spread0.217 · 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.

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

Citations8
Published2018
Admission routes1
Has abstractyes

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