Bibliographic record
Abstract
Kanisha Bembridge is a full-time faculty member with the College of Aeronautics at Embry-Riddle Aeronautical University (ERAU) Worldwide specializing in aviation safety and human factors. She also has the privilege of serving as an Associate Program Coordinator for the Bachelor of Science in Aeronautics (BSA) degree program. Her experience as an aviation professional spans just over 19 years, holding leadership positions at major airlines such as Air Canada, Lufthansa German Airlines, and Express Jet (Delta Connection). She worked as an Aviation Safety Trainer for above and below wing airline operations and facilitated Crew Resource Management (CRM) training for airline crew. Her career in aviation then transitioned to airport operations and emergency management at Hartsfield-Jackson Atlanta International Airport, where she helped to manage the day to day operations of the World’s Busiest Airport and coordinated with various agencies and stakeholders in response to incidents and major events. Mrs. Bembridge was awarded a Bachelor of Science in International Relations from the University of the West Indies (Mona Campus) and a Master of Science in Aviation Safety Management from the University of Central Missouri. She also holds graduate and professional certifications in Project Management and Emergency Management.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.147 | 0.063 |
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".