Community clusters in wildlife and environmental management: using TEK and community involvement to improve co-management in an era of rapid environmental change
Bibliographic record
Abstract
Environmental change has stressed wildlife co-management systems in the Arctic because parameters are changing more rapidly than traditional scientific monitoring can accommodate. Co-management systems have also been criticized for not fully integrating harvesters into the local management of resources. These two problems can be approached through the use of spatiallydefined human social units termed community clusters, which are based on the demographic or ecological units being managed. An examination of polar bear management in Nunavut Territory, Canada, shows that community clusters provide a forum to collect and analyse traditional ecological knowledge (TEK) over a geographic area that mirrors the management unit, providing detailed information of local conditions. This case study also provides examples of how instituting community clusters at a governance level provides harvesters with social space in which to develop their roles as managers, along the continuum from being powerless spectators to active, adaptive co-managers. Five steps for enhancing co-management systems through the inclusion of community clusters and their knowledge are: (1) the acceptance of TEK, science, the precautionary principle and the right of harvesters not to be constrained by overly-conservative management decisions; (2) data collection involving TEK and science, and a collaboration between the two; (3) institutionalization of community clusters for data collection; (4) institutionalization of community clusters in the management process; and (5) grass-roots initiatives to take advantage of the social space provided by the community cluster approach, in order to adapt the management to local conditions, and to effect policy changes at higher levels, so as to better meet local objectives.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".