"Whose community?" : the political ecology of community forestry in British Columbia's Sea-to-Sky Corridor
Bibliographic record
Abstract
With the mounting unpredictability of climate change and an international commitment to reducing greenhouse gas emissions, the community forestry model is increasingly being adopted by British Columbian communities attempting to take charge of climate adaptation and mitigation in their own locales. Although the benefits of community-based natural resource management are largely agreed upon, some political ecologists believe that the community forest model may sometimes deepen existing inequalities and serve powerful interests. This dynamic is particularly contentious in Canada, where Crown land and Indigenous traditional territories overlap, and particularly in British Columbia, where most First Nations never signed formal treaty agreements with colonial forces. This interdisciplinary research uses a modified version of the Teitelbaum framework to build a qualitative political ecological analysis of participatory governance, rights, local benefits, ecological benefits and relationships in the Cheakamus Community Forest. In the case study, the key finding across these indicators is that while there is a singular community forest, there is not a singular community, leading to differential experiences of access and control. Nonetheless, this research shows how the community forest model remains promising and is in a state of transformation that parallels shifting sociocultural values.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".