ICAO Language Proficiency Requirements and the training results of Korea Air Traffic Controller
Bibliographic record
Abstract
ABSTRACT 영어는 1947년부터 국제민간항공기구에 의해 국제 항공공통어로 사용되기 시작하였다. 그러나 원어민을 제외한 대부분 국가의 조종사와 항공교통관제사는 항공영어로 인한 어려움을 토로하고 있으며 항공기 사고의 많은 부분도 항공영어를 사용하는 communication 문제로 발생하고 있다. 이 점을 인식한 ICAO에서는 2008년부터 항공영어의 등급을 제도화하여 비영어권 국가의 항공종사자에 대한 영어능력의 향상을 도모하고 있다. 본 연구는 관제사를 대상으로 한 항공영어교육의 결과를 SPSS11을 이용하여 분석하였다. 분석결과 교육기간과 교육시간이 영어성적 결과에 유의미한 차이를 보이며, 항공영어의 평가요소 6가지가 상호 유의미한 영향을 미치는 것으로 분석되었다.Keywords : ICAO(국제민간항공기구), Aviation English(항공영어), pilot(조종사), air traffic controller(항공교통관제사), language proficiency rating(영어능력평가등급) Ⅰ. Introduction First operation of B707 in 1960 was the trigger to transform the operation type from short distance flight to long and mass air transportation. Hence demands for the internationally standardized phraseologies are required. In this end, ICAO decided English as a standard language for the international operation in October 1947. However, among all UN members, native English speaking countries are limited to UK, USA, Canada, Australia, New Zealand, Ireland, and South Africa. The rest countries are either bilingually use it or learn it as a second language. This is the reason a number of pilots and air traffic controllers of many countries have difficulties in using English. Communication problems between pilots and controllers in aviation English are occurring
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".