INVESTIGATING THE CONSTRUCT OF AERONAUTICAL ENGLISH LISTENING TESTING: A QUALITATIVE ANALYSIS OF THE ICAO RATING SCALE
Bibliographic record
Abstract
Since the publication of the International Civil Aviation Organization (ICAO)’s language proficiency requirements, a number of different tests have been developed and implemented around the world to assess pilots and air traffic controllers’ proficiency in English. Meanwhile, researchers have questioned the clarity and appropriateness of the policy, and the reliability of tests (e.g., Alderson 2011; Douglas 2004; Emery 2014). ICAO has recently acknowledged that, over the years, multiple interpretations of the policy have led to practices that might undermine the meaningfulness of aviation English tests (ICAO 2022b). However, the ICAO Rating Scale remains as the instrument to be used in assessing pilots and air traffic controllers’ aeronautical language proficiency. Thus, this article explores the construct of aeronautical English listening tests stated in the comprehension descriptors of the ICAO rating scale, as well as the elements of the other descriptors that may inform the definition of this construct. An in-depth content analysis of the rating scale was conducted by using the “interview technique”, as described by O’Leary (2021). Results provide useful information for the development of listening tests in the aeronautical context. A better interpretation of the construct informed by the policy can help to reduce the differences among test implementations around the world and further contribute to more standardized and meaningful testing practices.
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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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".