Bibliographic record
Abstract
Recently, as Korea's exports and economic growth have decreased, concerns over the economy are growing. The main reason for the slump of export is the concentration of export countries and export products. In fact, the proportion of exports to China was 26.8% and that of semiconductors was 20.1% among the total exports of Korea in 2018. In the first half of 2019, exports to China and semiconductors declined by -16.9% and -24.0% respectively. As a result, domestic economic growth in the first quarter was -0.4%. So, in this paper examine the recent export trends and calculate the degree of concentration by item and country using the indicators of HHI and CR. And, analyze the effects of such concentration on export and economy of Korea. Then, the necessity of export diversification by item and region and detailed export expansion methods are presented. This paper is timely in the face of sluggish exports in Korea. And differs from previous studies in that it proposes the necessity of diversification of exports and proposes expansion strategy of export portfolio. It is expected to contribute to the export expansion strategies and export policies of individual companies and governments.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".