An Analysis on the Competitiveness and Specialization Levels of the Countries in the Export of Wood and Articles of Wood
Bibliographic record
Abstract
The main aim of this study is to determine the export competitiveness and specialization levels of countries that export wood and wooden products. To do this, 10 countries with the highest export volume between 2010 and 2019 are determined under the HS-2007 product classification, using the “44-Wood and Articles of Wood” product group export data. The Relative Export Advantage (RXA) Index and Net Export Index (NEI) are used to measure export competitiveness. Moreover, for the product groups where countries gain competitive advantage, cross-country correlation is analyzed. With the analysis, efforts are made to determine whether there is a correlation between the countries’ specialization coefficient. On the one hand, it is seen that, among 21 product groups under the 44-Wood and Articles of Wood group, Poland has a competitive advantage in the export of 16 product groups, followed by Malaysia in 13, Austria and Vietnam in 12, and Canada and Indonesia in 11, respectively. Germany and USA, on the other hand, have competitive advantages in six product groups which make them the least advantageous among all these countries. Countries that have had a competitive advantage have usually shown specialization in exports as well with respect to their own trade performance. According to the results of the correlation test, which is between specialization coefficient within a certain product group, a strong correlation and a positive relationship are found between the countries that have a competitive advantage in exports. Especially in the product groups coded 4409 (wood, including strips and friezes for parquet flooring, not assembled), 4411 (fiberboard of wood), 4415 (packing cases, boxes, crates, drums, and similar packings, of wood), and 4418 (builders’ joinery and carpentry of wood), a high correlation exists between countries. To be specific, in the correlation test held for 10 countries and 21 product groups, Malaysia, matching in 40 categories with the other countries, has the greatest number of meaningful relationships. It is followed by Austria with 37 and Poland with 32. Therefore, it could be stated that this is a confirmation that these countries, in a high competition with the other countries, have a meaningful relationship in terms of specialization coefficient in the global market.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".