To log or not to log? How forestry fits with the goals of First Nations in British Columbia
Bibliographic record
Abstract
Commercial forestry has played an important role in the Canadian economy. Yet, First Nations (FNs) communities have not shared equitably in the benefits. Since 2002, the government of British Columbia (BC) has actively sought to address this inequity by increasing the volume of forest harvesting tenures to FNs. The rationale is that rights to harvest will also enhance economic and then social outcomes, as well as address broader legal and political disputes. However, whether these rights can translate into the expected benefits has received little attention. This paper seeks to help address this knowledge gap by interviewing FNs experts and forestry professionals in BC to understand the long-term goals of FNs in forestry, to strategically evaluate how (and if) opportunities from forestry arise, and to identify institutional factors that influence successful participation in forestry. What we found is that forest tenure can promote economic outcomes, but it often comes at the expense of other intrinsic forest values. We conclude that a rights-based approach alone will not achieve the diverse outcomes related to forestry without effective governance by FNs to evaluate and capitalize on the opportunity in ways that are legitimate to the individual community’s 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 | 0.002 | 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 | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".