The effects of the 2008 Lacey Act amendment on international trade in forest products
Bibliographic record
Abstract
Despite international efforts, illegal logging continues on a scale that is of global concern, with significant volumes of illegally harvested wood entering into international trade flows. Recently, major importers of forest products have implemented timber legality legislation prohibiting the possession and/or importation of wood and wood products that are of illegal origin. Drawing on bilateral trade data and using a quantitative, regression-based comparative case study methodology, the effects of the 2008 Lacey Act amendment on the international trade of forest products were evaluated. A data-driven method was used to create aggregate control groups for comparisons with countries affected by the policy. If the policy has been effective in reducing the volume of illegally harvested forest products being imported into the U.S., we would expect to see some unique differences in post-policy U.S. imports of wood and wood products from areas with high levels of suspicious wood in their supplies. Results from these analyses show few substantial differences in post-policy imports of wood products of suspicious origins into the U.S. However, the results suggest that the policy may be affecting wood imports by major exporters of finished wood products to the U.S.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".