A Comparison of the International Competitiveness of Forest Products in Top Exporting Countries Using the Deviation Maximization Method with Increasing Uncertainty in Trading
Bibliographic record
Abstract
Increased uncertainty in the trade environment has become a reality. However, so far, there is no well-established indicator system to quantify the international competitiveness of forest products in the context of increased uncertainty in the trade environment. Based on expanding the concept of international competitiveness, we constructed an evaluation indicator system of international competitiveness including market performance and competitive advantage, which highlighted market stability and market sustainability indicators. We obtained a comprehensive international competitiveness index of the forest products by Deviation Maximization Method. This study aims to compare and evaluate the international competitiveness of forest products in the top 10 exporting countries using a comprehensive international competitiveness index. The results showed that it is more accurate and comprehensive to use the comprehensive international competitiveness index to evaluate the international competitiveness of forest products, compared to using only a single index. Additionally, the changes to the composite index of international competitiveness went hand-in-hand with the uncertainties the observed countries face, indicating that the indicator system is applicable to the measurement of international competitiveness in an uncertain environment. Large differences exist in the level of international competitiveness of forest products among observed countries. German paper products and wood chips, Chinese wood furniture, wood-based panels and wood products, U.S. logs and wood pulp, and Canadian sawn wood were the most competitive. On the whole, China, Germany and Italy have the highest level of overall international competitiveness in forest products, with Brazil and Poland showing the most significant increases.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".