ARE LOG MARKETS COMPETITIVE? EMPIRICAL EVIDENCE AND IMPLICATIONS FOR CANADA-U.S. TRADE IN SOFTWOOD LUMBER
Bibliographic record
Abstract
Under the U.S. Department of Commerce's 'changed circumstances' review, it is possible that the countervail duty on Canadian lumber can be lowered if administered stumpage prices are based on transaction evidence appraisal - on actual auction data and regression analysis. The Province of British Columbia is implementing such a marketbased approach to set stumpage fees, relying on timber auction data from the Small Business Forest Enterprise Program (SBFEP) and OLS regression. We employ SBFEP data to estimate a truncated regression model, comparing our estimates of stumpage fees with the OLS results. It turns out that the OLS approach is biased and likely results in overestimates of stumpage in some timber stands and underestimates in others. Further, we demonstrate that number of bidders has an important impact on bids, but that this could create even more problems for resolving the trade dispute.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".