A Worldwide Review of Selection for Air Traffic Control Personnel
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
Air traffic control is a highly technical occupation that requires emotional stability, considerable aptitude, and lengthy training. Identifying those individuals with the greatest potential to capitalize on training is a major interest of air traffic organizations around the world, particularly when considering limited resources. This paper compares and contrasts several selection systems, to include their development, continuing validation, and in one case, demise. In the erstwhile, two-stage US Federal Aviation Administration (FAA) selection process, applicants completed the written Office of Personnel Management (OPM) test battery and a nine-week screening program at the FAA Academy in Oklahoma City, OK. The eventual replacement to this system, the Air Traffic Selection and Training (AT-SAT) computerized test battery, is now used to assess aptitude for air traffic control duties. The US Navy and Air Force’s use of composites from the Armed Services Vocational Aptitude Battery (ASVAB) is next explored. The computerized battery employed by EUROCONTROL, termed the First European Air traffic Selection Test (FEAST), is then considered. FEAST is used by many European countries to complement their existing selection methods. To the delight of researchers worldwide, users are required to agree to assist in the continuing validation of FEAST. Finally, the approach used by SHL Canada to recruit and select trainees for NAV CANADA Air Traffic Control positions using a variety of cognitive ability and personality measures is described, including the associations found between cognitive measures, ability tests, and performance in both initial and on-the-job training.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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