Animal agency: Wildlife management from a kincentric perspective
Bibliographic record
Abstract
(Uploaded by Plazi for the IPBES Invasive Alien Species Assessment) 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. © 2017 Welti et al.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".