Bibliographic record
Abstract
Dr. Michael Mineiro holds a Doctor of Law (McGill University, 2011), a Legal Masters in Air and Space Law (McGill University, 2008), Juris Doctor (University of North Carolina, 2005), and a Bachelor of Arts in International Relations (North Carolina State University, 2001), with additional studies undertaken at the Hague Academy of International Law (2010), Beijing Language and Culture University (2005), and the University of Hong Kong (2004). He is a published scholar in leading space law and policy journals, including the Journal of Space Policy and Journal of Space Law. Dr. Mineiro is regularly invited to lecture on international space law, space policy, and commercial remote sensing regulation at space law faculties, including Georgetown University, the International Space University, McGill University, and the University of Colorado. He currently holds an appointment to the Institute for Defense Analysis, Science and Technology Policy Institute (STPI), a Federally Funded Research Development Center (FFRDC) that provides objective analysis of science and technology (S&T) policy issues for the White House. In this capacity, Dr. Mineiro works on space law and policy issues with agencies across the Federal Government, including the Department of Defense (DOD), National Aeronautics and Space Administration (NASA), and the National Oceanic and Atmospheric Administration (NOAA). Dr. Mineiro also serves as an expert on law and regulatory matters to the U.N. Committee on the Peaceful Uses of Outer Space (UNCOPUOS) Working Group on Long-term Sustainability of Space Activities. Prior government appointments include service as an international relations specialist at NOAA’s Environmental Satellite, Data, and Information Service (NESDIS) and as an attorney with NOAA’s Office of General Counsel.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.159 | 0.104 |
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".