Bibliographic record
Abstract
There is an expanding global network of free trade agreements (FTA). High-quality, comprehensive free trade agreements play an important role to support global trade liberalization and are explicitly allowed under the World Trade Organization (WTO) rules. An FTA is an international treaty that removes barriers to trade and facilitates stronger trade and commercial ties that contribute to increased economic integration between participating countries. Korea benefits from the global FTA trend; however it has started and developed FTA negotiations later than other countries. Current FTA agreements exist with Chile, Singapore, EFTA, ASIAN, India, EU, Peru, USA, Turkey, Australia, and Canada; in addition, there are ongoing negotiations with China, Colombia, New Zealand, and Vietnam. FTA open up opportunities for the textile/clothing industry to expand businesses into key overseas markets. FTA improve market access across all areas of trade to help maintain and stimulate the competitiveness of textile/clothing firms. This study examines the expansion of free trade agreements in light of changes in the international trade environment and the status of the Korean textile/clothing industry. Korea``s textile/clothing export/import products and concession of tariff, country of origin covered under Korea-US/China FTA are investigated to identify problems. This study provides practical and policy implications for the textile/clothing industry in regards to the Korea-US/China FTA.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.014 |
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".