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Record W2765597957 · doi:10.1002/ecs2.1978

Animal agency: wildlife management from a kincentric perspective

2017· article· en· W2765597957 on OpenAlex
Jonaki Bhattacharyya, Scott Slocombe

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEcosphere · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsWilfrid Laurier UniversityUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaHakai InstituteUniversity of Waterloo
KeywordsAgency (philosophy)IndigenousWildlife managementWildlifeEnvironmental resource managementParallelsTraditional knowledgeEmpirical researchAdaptive managementSociologyPolitical scienceEnvironmental planningEcologyEnvironmental ethicsGeographySocial scienceBiologyEngineering

Abstract

fetched live from OpenAlex

Abstract Co‐management of wildlife and landscapes often requires managers to work with Indigenous and conventional Western worldviews. Many cultures recognize animals as non‐human persons with decision‐making agency. Such perspectives, termed “kincentric ecology,” suggest a relational approach to management that differs from convention in North America. We argue that kincentric perspectives are highly relevant to current approaches and issues in wildlife management, including the incorporation of Indigenous Knowledge. Using empirical research with the Xeni Gwet'in First Nation in British Columbia, Canada, we discuss four dimensions of kincentricity key to collaborative management, with notable parallels in emergent systems science: (1) shift in emphasis from human rights to responsibilities; (2) focus on social–ecological systems; (3) acknowledgment of uncertainty and rapid change; and (4) emphasis on locally relevant, empirical knowledge. Wildlife and land management influenced by bioculturally diverse knowledge implies a more systemic approach; adaptive processes; changed goals and values; and shifting responsibilities among stakeholders.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0230.008

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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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