Bibliographic record
Abstract
Debate on the effect of increasing number of multilateral convention on environment and environmental protection policy to international trade and national productivity have become prominent in the environmental trade literature. There are more than 220 multilateral convention and forty new environmental trade barriers have been imposed over the last decade and many of convention and regulation could impact on international trade. Export oriented countries such as India, China, Taiwan, and Korea may have difficulties to their national export and their environmental protection expenditure have been increased. Thus, this research have been explored the relationship between effectuation of most effective multilateral convention on environment and increasing environmental protection expenditure and export volume of five major export industries. The Basel Convention, Montreal Protocol, and UN Framework Convention will be tested as most effective multilateral convention on environment to export. With Korean time series data ranging from 1976 through 2004, the estimation results show that effectuation of most effective multilateral convention on environment and increasing environmental protection expenditure may increase trade volume on five major industries. Some researchers argue that introducing stringent environmental policies could promote export growth by introducing and transferring more efficient production technologies and it makes industries more competitive. These findings are comparatively robust for petrochemistry and electronic and electricity industry, but less do for textile, steel, and transport equipment industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".