Bibliographic record
Abstract
Dr. Ruth Stilwell is the Executive Director of Aerospace Policy Solutions LLC, an adjunct professor at Norwich University, and Senior Nonresident Scholar at the Space Policy Institute of George Washington University. She is one of the world’s leading authorities on integrated space and aviation policy and governance. A 25-year air traffic controller, experienced labor leader and policy expert, Dr. Stilwell is also an accomplished researcher and lecturer. Her numerous publications and presentations, which cover a wide range of space and aviation, public safety, human factor, administration, financing, and industry reform topics. In addition to her publications, she is a contributing author to McGill University’s Global Space Governance: An International Study. Dr. Stilwell’s specific areas of expertise include: integrating commercial space operations in civil airspace; projecting air traffic controller retirement and staffing requirements; FAA funding and financing structures; workers’ rights and more. Dr. Stilwell served from 2010-2015 as the industry expert representing air traffic controllers on the International Civil Aviation Organization (ICAO) Air Navigation Commission in Montreal. Her air traffic control experience includes 25 years of operational duty at the Miami Air Route Traffic Control Center, two years as liaison to the FAA Requirements Service, and six years as Executive Vice President of the National Air Traffic Controllers Association. The founding chairperson of the Air Traffic Services Committee of the International Transport Workers Federation, a position she held for four years, Dr. Stilwell currently serves on the Technical Committee on Human Spaceflight Safety of the International Association for the Advancement of Space Safety, which is working to develop safety guidelines for human commercial spaceflight. Dr. Stilwell earned her BA in Labor Studies at the National Labor College and her MPA and DPA at the University of Baltimore. A resident of Florida, she enjoys skiing, long-distance running, and spending quality time with her stepson.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.329 | 0.186 |
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".