Measuring the economic impacts of trade liberalisation on forest products trade in the Asia-Pacific region using the GTAP model
Why this work is in the frame
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Bibliographic record
Abstract
SUMMARY The paper examines the effects of unilateral trade liberalisation of forest products amongst the Asia-Pacific Economic Cooperation (APEC) member countries. It attempts to quantify the gains from liberalised trade when APEC member countries extend their preferential treatment to non-member countries in forest products trade using the Global Trade Analysis Project (GTAP) model. Given that forest products comprise only a relatively small proportion of world merchandise trade, it is expected that trade liberalisation would cause small changes in terms of trade, real GDP, production, consumption and prices of forest products amongst APEC member countries. The results suggest that in general, when more countries participate in trade liberalisation the more welfare could be improved with the exception of the region North America which is comprised of three countries, the United States of America, Canada and Mexico.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it