Action on Sustainable Forest Management through Community Forestry: The case of the Wetzin'kwa Community Forest Corporation
Bibliographic record
Abstract
Community forestry is a collaborative governance approach to forest management that is seen as a promising tool for implementing sustainable forest management. The expectation of government is that community forests, as managers of public forestlands, will work to achieve sustainability. Little has been written, however, about the ways community forests are being managed in an effort to realize this goal. This paper considers how the Wetzin'kwa Community Forest Corporation (WCFC) in British Columbia is working to sustainably manage its community forest. The study identified efforts taken by the WCFC towards achieving sustainable forest management as perceived by participants, and considered these in relation to indicators established by the provincial government and the Canadian Council of Forest Ministers. The results reveal that WCFC is making progress towards sustainably managing the forest by taking action on issues such as local employment and protecting cultural values. However, it is difficult to make a definitive statement as to whether these efforts are modest or advanced, partly because the WCFC has not developed a set of criteria and indicators to measure its own performance, and also due to the lack of a clear framework from government for measuring the achievements of such small-scale forestry operations in relation to sustainability.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".