Benefits of collaboration between Indigenous and non-Indigenous communities through community forests in British Columbia
Bibliographic record
Abstract
When the Government of British Columbia (BC) introduced the Community Forest Agreement Program in 1998, it permitted a range of governance structures to allow flexibility and to learn which structures might be most appropriate for this new form of forest tenure. One structure that became fairly common was collaboration between Indigenous and non-Indigenous communities. This paper characterizes and analyzes the advantages of this collaborative governance model in three ways, identifying (1) what benefits this model entails, (2) how these are illustrated in three different community forests, and (3) how these forms of collaboration fit into a co-management spectrum. Because some of these benefits involve communities having greater degrees of power in forest governance, the model invites a consideration of the types of decision-making power experienced by Indigenous communities partnering or collaborating in BC community forests, as well as the types and range of benefits for all parties emerging from these collaborations. Fourteen indicators of the benefits of collaboration are identified, building on the discovery of five new benefits heretofore unrecognized in the literature. These understandings permit a more nuanced assessment of this particular type of co-management, leading to the generation of three new, broader hypotheses regarding the conditions that support co-management.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".