The softwood lumber dispute: Is a solution possible?
Bibliographic record
Abstract
Canadian exports of softwood lumber to the USA have long been a source of conflict. As the last Softwood Lumber Agreement expired, we examined this issue as a University class project in forest policy. This text presents our analysis of the history of this conflict and the positions of various actors, along with our twenty-year plan for resolving this conflict. Our strategy is in three phases, corresponding to our expectations of future events: five years of conflict and negotiation that we should use to prepare certain changes in our forest sector; ten years under a new agreement during which we can implement these changes; followed by another conflict that we hope Canada will win, resulting in lumber becoming just another traded commodity. Our policy has four key elements: a gradual transition to market-price systems for 55% of public forest lumber volume; an expansion of value-added transformation and diversification of products; a reduction of softwood lumber production; and, a coordination role for the Canadian Council of Forest Ministers. We do not believe that our proposition is perfect, but we consider that it offers an option for breaking out of the box that characterises this conflict.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".