1 A GENERAL EQUILIBRIUM ANALYSIS OF THE ECONOMIC IMPACT OF THE CANADIAN SOFTWOOD LUMBER TARIFF ON THE WASHINGTON ECONOMY
Bibliographic record
Abstract
Gilbert (Utah State) during the study. We thank Tom Wahl (IMPACT Center, WSU) for providing data and financial support. 2 A sixteen-sector computable general equilibrium model of the Washington economy was used to analyze the effects of the tariff on Canadian softwood lumber imposed in May 2002. Model results indicate that the tariff generates a 0.5 percent increase in Washington lumber output. Lumber imports from Canada decline by 26 percent, while the rest of the US lumber imports from Washington State increase by 5 percent. This illustrates an important distinction between national and regional trade policy analysis. At the state level, there are opportunities to substitute imports from the rest of the US for taxed foreign imports and thus moderate the negative economic impact of lumber tariff. Just as the lumber industry is advantaged by the tariff, the lumber using industries are damaged by the tariff. Counterfactual output reductions ranged between 0.5 percent and 1.5 percent in the downstream industries. On balance, once the Washington economy adjusts to a new equilibrium, the predicted change in gross state product is a very modest loss of roughly 0.002 percent
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".