INVESTIGATING THE CONSTRUCT OF AERONAUTICAL ENGLISH LISTENING TESTING: A QUALITATIVE ANALYSIS OF THE ICAO RATING SCALE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it