Relationships between Polarization and Openness in Korean Economy
Bibliographic record
Abstract
International trade usually changes the production patterns of an economy. The share of exporting industries tends to increase and that of importing industries tends to decrease. In the process of industrial restructuring, it is natural for the economy to experience a concentration toward exporting industries. At the same time, this concentration might also occur within separate industries; exporting firms tend to grow and the share of other firms tends to decrease. All these changes can result in a polarization of the economy. This paper investigates if this polarization trend occurred in the Korean economy by using industry and firm level data. In particular, we explore the question of whether there is any relationship between polarization and international trade, as there has been a lot of criticism focused on the idea that international trade has resulted in income inequality and polarization of the Korean economy. We calculated the GINI coefficient and other indices to measure the degree of polarization, and we performed regression analysis on the time series and panel data. This paper finds that there is a positive relationship between export ratio and polarization.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".