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 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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".