Substantiating the Strategic Directions of Development of the Woodworking Industry of the World Countries
Bibliographic record
Abstract
To determine the strategic directions of development of the woodworking industry of the country, a structural-logical scheme of scientific research is proposed, which includes the following stages: identification of the main substantive determinants of ensuring the development of the woodworking industry of the countries over the world; assessment of raw material potential and competitiveness of the woodworking industry of the world countries; modeling the impact of raw materials potential on the competitiveness of the woodworking industry in the countries of the world; determination of priority directions of development of the woodworking industry of these countries. An integral assessment of the raw material potential of the woodworking industry of the world countries was carried out by the following components: forest cover of the territory, reserves of the forest stand, the total volume of wood production, the volume of production of business wood, which made it possible to determine the level and disproportions of the development of raw materials for the woodworking industry of the countries of the world. According to the value of the integral indicator of the raw material potential of the woodworking industry in 2020, from 36 countries chosen, Finland, Canada, Sweden, Latvia, Estonia were included in countries with a high level of raw material potential of the woodworking industry, while the countries with the lowest level were Greece, Mexico, Italy, China, the Netherlands, and Ukraine. The level of competitiveness of the woodworking industry of Ukraine and the world countries is assessed. The leading countries in terms of competitiveness of the woodworking industry in 2020 included Brazil, Russia, Ukraine, Canada, Finland, while the countries with a low level of competitiveness of the woodworking industry included the Netherlands, Greece, Great Britain, Korea, Japan, and Italy. The carried out analysis allows to recommend for the group of leading countries in terms of competitiveness of the woodworking industry (including Ukraine) to focus on increasing exports of woodworking goods with high added value, such as sheet wood materials. A modeling of the influence of raw material potential on the level of competitiveness of the woodworking industry of the world countries is fulfilled. It is determined that the strategic directions of development of the woodworking industry of the countries of the world are to increase the output of products with high added value and the introduction of measures for the rational use of forest resources.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".