Erratum: Welfare impacts of the 1996 United States Canada Softwood Lumber (trade) Agreement
Bibliographic record
Abstract
Note: Values are in millions of 1997 U.S. dollars. This change in Canadian producer surplus only accounts for Canadian exports to the U.S. market and does not cover Canadian domestic and other foreign markets for Canadian softwood lumber. In theory, the SLA should reduce softwood lumber prices in the two (domestic and other foreign) markets, which in turn lower producer surplus (and raises the amount of consumer surplus) in these markets. Since the primary focus of this paper is on U.S. market, we merely point out that a full welfare analysis for Canadian producers must include all three markets. Thus, the net Canadian producer surplus, including all three markets, should be lower than what we have estimated in this paper. At one extreme, Canadian producers could experience a net decrease in well-being as result of decrease in price and output of their softwood lumber in their own domestic and other exporting markets, even they are better off in the U.S. market with the SLA. Although this extreme scenario is not likely to be true because the U.S. market (accounts for 65% Canadian production) is the largest of the three markets for Canadian softwood lumber, a further study could clarify this issue. This number is calculated based on data in Table 1. Other producer surplus is the gain of Canadian exporters in provinces that are not covered by the SLA and of exporters from other countries that supply softwood lumber in the U.S. market. Table 5. Estimated welfare impacts for the first 4 years under SLA.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".