Bibliographic record
Abstract
Michael S. Dodge currently serves as an Assistant Professor & Graduate Program Director in the Department of Space Studies at the University of North Dakota. Prof. Dodge received his LL.M. degree in Aviation & Space Law from McGill University in the Fall of 2011 (thesis: “Global Navigation Satellite Systems (GNSS) and the GPS-Galileo Agreement”). Before attending McGill, he obtained his J.D. in 2008 from the University of Mississippi School of Law, where he was also the first recipient of the Certificate in Remote Sensing, Air, and Space Law. He obtained dual degrees in B.S. (in Biological Sciences) and B.A. (in Philosophy) in 2005, from the University of Southern Mississippi. Prof. Dodge teaches several courses for Space Studies, including Space Politics and Policy (SpSt 560), Space Law (SpSt 565), and Remote Sensing Law and Policy (SpSt 575). These courses include a multitude of historical, political, and legal facets to space activities, and cover subjects such as legal issues in space exploration; regulation, privacy law, and Constitutional concerns surrounding the use of remote sensing technology; licensing and regulatory requirements for space activity; the historical and evolutionary nature of space policy (both nationally and internationally); public international law; and domestic United States legal governance of space activity. Prof. Dodge’s research has included GNSS law, remote sensing law & regulation, environmental regulation of outer space, concepts of sovereignty and ownership rights in space, and the nexus of remote sensing technology with global humanitarian law and disaster relief law. Future studies include examination of future environmental regulatory structures for orbital space, as well as domestic United States legislation and its relationship with the precept of non-appropriation in outer space, including an analysis of the ownership of celestial resources from potential asteroid mining operations.
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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 | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.260 | 0.154 |
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".