Bibliographic record
Abstract
After The Korean government enacted the Part VI of Korean Commercial Code(herein after as “KCC”) as the Part of the “Carriage by Air” and the enactment of the Part VI is effective from November 24, 2011. Even though the Korean government ratified the Montreal Convention of 1999(Convention for the Unification of Certain Rules for International Carriage by Air, Signed at Montreal on 28 May 1999) on September 20, 2007 that became effective from December 29, 2007, there have not been any laws or regulations applying to the domestic air for damages of goods which are caused by domestic air transportations. Moreover, the volume of air cargo transportation in Korea has increased in recent years nevertheless the KTX(Korean high-speed railroad) has been tremendously developed and expanded its routes all over the country. Therefore, the needs for some laws or regulations on the domestic air carriage including air carriers liability for damages of goods has been increased as well in Korea. Under this circumstance, the Korean government has seriously deliberated on the subject of the needs and finally decided to enact the Part of the “Domestic Carriage by Air Act” in the KCC. This Article examines the newly enacted provisions of the KCC, focusing on the Air Carriers Liability for Damages of Goods which mainly reflected the Montreal Convention. Through the examinations, this Article tries to suggest some betterments of the enactment of KCC as well.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".