Bibliographic record
Abstract
Korea has established unification community for thousands of years, thus it differentiates from other divided nations, which has been improvised within recent years. Even without considering thousands of years, there are many separated families in South and North Korea. However, there is an increase in negative perspectives on unifications. But if we take account of advantages of unification, this is not rational. The unification cost varies from 50 billion dollars to 5 trillion dollars depending on people who estimates the cost. If we think about the fact that construction of 1 bridge of Han River costs about 1 billion dollars, then total unification costs will be close to 5 trillion dollars. If we understand that total income of North Korea has to be raised up to 2/3 of South Korea income, and that population is half of South Korea, then current North Korea’s GNP of 20∼30million dollar has to be one quarter of 1 trillion dollar, which is South Korea’s current GNP, which means that regional GNP of North Korea has to be 220∼230 billion dollars which leads to investment of 5 trillion dollars to achieve the GNP. However, advantages of unification exceeds this investment amount, greatly. Decrease in the cost of national defense and war risks as an additional advantage, the fact that we can achieve 1 hundred million population which is known to be optimum size of a nation, it is a national advantage in north east asia, where many large countries are located. There are still many obstacles to overcome in order to achieve unification which is beneficial to both South and North Korea. Land policy in order to earn unification finance, is one of the main procedures that needs to be through before gaining advantages of unification. For example, Germany spent a lot of costs in order to compensate for the land. Many people compare the cases of Germany, but the unification of Germany differentiates greatly from Korea. For Germany, country has been divided while country was still continuing, so that they had to return the land to the old owner. But for North and South Korea, we conducted agrarian and land reform, thus the ownership has been redistributed after the reform. Thus it is obvious that there is no need to compensate or redistribute the land to the old owner, and this fact does not change on decisions whether either side decides to carry on or not to carry on the policies established by one side. This is the reason why I support the return of the ownership of the land should not be an obstacle of unification, and this seems reasonable. Therefore, we should now focus on the increasing the welfare of the nation and redesign of the nation in preparation for unification.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".