U.S.-Canada softwood lumber trade disputes and lumber price volatility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper uses analysis of variance (ANOVA) and regression analysis to study U.S. softwood lumber price volatility between 1980 and 2000, the period that coincides with several episodes of U.S.-Canada softwood lumber trade disputes. The results show that softwood lumber prices were more volatile in the 1990s than in the 1980s, with the period between 1991 to 1996 being the most volatile, the period covered by the 1996 U.S.-Canada Softwood Lumber Trade Agreement (SLA) being the second most volatile, and the period covered by the U.S.-Canada Memorandum of Understanding being the least volatile. Uncertainty, supply constraints due to the SLA and declining availability of federal timber in the western United States, and variations in housing starts were the primary causes of price volatility in the 1990s. The results of this paper have implications on resolving the U.S.-Canada softwood lumber trade disputes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it