Community clusters in wildlife and environmental management: using TEK and community involvement to improve co-management in an era of rapid environmental change
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it