MétaCan
Menu
Back to cohort
Record W4362016860 · doi:10.58940/2374-6793.1768

Native Language Effects on Flight Training Performance

2022· article· en· W4362016860 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Aviation Aeronautics and Aerospace · 2022
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsFlight trainingAviationAir traffic controlLanguage proficiencyAviation safetyAeronauticsTraining (meteorology)Language assessmentComputer sciencePsychologyFlight simulatorEnglish languageEngineeringSimulationMathematics educationAerospace engineeringGeography

Abstract

fetched live from OpenAlex

Several high-profile commercial aviation accidents in the past that were caused in part by inadequate English language proficiency confirmed the importance of clear and concise communication between air traffic controllers and pilots. Although the connection between English language proficiency and aviation safety has been well established, there has been very little research concerning the relationship between English language proficiency and flight training performance. Thousands of international students who are not native speakers of the English language come to the United States and Canada for ab initio flight training every year. While the ability to communicate with air traffic controllers is critical for the safety of flight, communication skills can also have a profound effect on flight training performance. International flight students not only must communicate with air traffic controllers, but they must also communicate with their flight instructors on the ground and in flight. In addition, they must also be able to read and understand textbooks, manuals, and check lists that are all written in the English language. This research is focused on the relationship between English language proficiency and performance in ab initio flight training programs.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.205
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it