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Air Traffic Communication in a Second Language: Implications of Cognitive Factors for Training and Assessment

2008· article· en· W1885693054 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTESOL Quarterly · 2008
Typearticle
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsConcordia UniversityUniversité du Québec
Fundersnot available
KeywordsWorkloadFluencyLanguage proficiencyAir traffic controlMandarin ChineseTask (project management)PsychologyCognitionSpeech productionComputer scienceSpeech recognitionLinguisticsMathematics educationEngineering

Abstract

fetched live from OpenAlex

This study investigated the effects of second language (L2) proficiency and task‐induced cognitive workload on participants' speech production and retention of information in an environment designed to simulate the demands faced by pilots receiving instructions from air‐traffic controllers. Three groups of 20 participants (one native‐English‐speaking group, two native‐Mandarin‐speaking groups of relatively high and low levels of English proficiency) played the role of pilots. Participants listened to, repeated, and responded to simulated air‐traffic controller messages (in English) under conditions of low and high workload. In the high workload condition, participants performed a concurrent arithmetic task while repeating the messages. The dependent variables were message repetition accuracy and speech production (accentedness, comprehensibility, fluency, as perceived by 10 native‐English‐speaking raters). The native English speaker group repeated messages more accurately than both L2 groups, and the low‐proficiency group repeated messages less accurately in the high workload condition than in the low workload condition. The native speaker and the low‐proficiency groups were perceived as less fluent in the high than in the low workload condition, and only the low‐proficiency group's speech was perceived as more accented in the high than in the low workload condition. Implications for language training and assessment for English for specific purposes are discussed.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.274

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 head0.043
GPT teacher head0.307
Teacher spread0.264 · 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