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Record W3133854128

Conservation, Hunting Policy, and Rural Livelihoods in British Columbia

2021· article· en· W3133854128 on OpenAlexaffvenueabout
Kelsey Boule, Josie V. Vayro, Courtney W. Mason

Bibliographic record

VenueJournal of rural and community development · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsLivelihoodWildlifeSustainabilityGeographyIndigenousWildlife managementWildlife conservationNatural resourceEnvironmental planningNatural resource managementEnvironmental resource managementCommunity-based conservationPolitical scienceEcologyAgricultureEconomics
DOInot available

Abstract

fetched live from OpenAlex

Hunting as a wildlife conservation tool has been the centre of much debate as climate change and increased pressure from human encroachment continue to impact wildlife species globally. As ongoing land use conflicts, natural resource extraction, and population growth threaten habitat, leaders face a dilemma around how to balance sustainable land use management while supporting rural economies. We explored the role of hunting in conservation and looked critically at the perspectives of hunters and those involved in hunting in the western Canadian province of British Columbia. A community-based participatory research methodology guided this study, and we collected data through semi-structured interviews with resident hunters, conservation officers, wildlife biologists, guide outfitters, hunting suppliers, and Indigenous hunters. The results can help inform inclusive policies that balance the needs of local peoples, communities, and conservation in rural regions. Diverse hunting stakeholders have unique knowledge of regional lands and wildlife management practices that are integral to socio-economic and environmental sustainability in rural regions. Keywords: hunting, conservation, community-based participatory research, rural livelihoods; British Columbia

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 score0.167
Threshold uncertainty score0.987

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 studies0.0000.000
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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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

Citations3
Published2021
Admission routes3
Has abstractyes

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