Encouraging the Transition to Sustainable Forestry in Canada with Ecological Fiscal Reform – Potential and Pitfalls
Bibliographic record
Abstract
There has been some slow but steady progress on sustainable forest management (SFM) in Canada over the last decade. While the change is positive, it is insufficient to keep up with the multiple demands on forest ecosystems. This paper argues that while the provinces should work towards fundamental regulatory reform in the forest sector (such as reforming tenure structures to better reflect Aboriginal, social and ecological needs and values), the federal government should concomitantly offer short-to-medium term fiscal incentives to encourage forest companies operating in Canada to voluntarily move faster and closer towards SFM. The paper argues that such measures would also serve to help level the playing field between leading forest companies (who are facing increased costs from implementing SFM) and other companies who continue to operate at the regulatory baseline. The paper discusses some of the challenges of using fiscal incentives (such as WTO subsidy rules) and offers suggestions for ensuring the measures avoid these pitfalls.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| 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".