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Record W2138734889 · doi:10.5751/es-02892-140142

Respect for Grizzly Bears: an Aboriginal Approach for Co-existence and Resilience

2009· article· en· W2138734889 on OpenAlexafffundvenueabout
Douglas A. Clark, D. Scott Slocombe

Bibliographic record

VenueEcology and Society · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsWilfrid Laurier University
FundersAurora Research InstituteAlberta Conservation Association
KeywordsUrsusRiparian zoneWildlife managementWildlifeTerminologyEnvironmental resource managementGeographyPsychological resilienceGrizzly BearsHuman–wildlife conflictEcologyEnvironmental ethicsSociologyPsychologySocial psychologyBiologyHabitat

Abstract

fetched live from OpenAlex

Aboriginal peoples' respect for grizzly bear (Ursus arctos) is widely acknowledged, but rarely explored, in wildlife management discourse in northern Canada.Practices of respect expressed toward bears were observed and grouped into four categories: terminology, stories, reciprocity, and ritual.In the southwest Yukon, practices in all four categories form a coherent qualitative resource management system that may enhance the resilience of the bear-human system as a whole.This system also demonstrates the possibility of a previously unrecognized human role in maintaining productive riparian ecosystems and salmon runs, potentially providing a range of valued social-ecological outcomes.Practices of respect hold promise for new strategies to manage bear-human interactions, but such successful systems may be irreducibly small scale and place based.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0130.040
Scholarly communication0.0060.005
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.276
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations45
Published2009
Admission routes4
Has abstractyes

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