Lessons from International Relations Theory and Terrestrial History for a 21st-Century Moon Race
Bibliographic record
Abstract
Space agencies from the U.S., Europe, Japan, China, Russia, and beyond are preparing to take another giant leap for humankind: establishing permanent research stations on the Moon. These efforts raise important questions for researchers across many fields, including international relations, business, astronomy, and law. This thesis focuses on the international relations element, and specifically two questions: How have allied and competitor countries worked together in global commons domains where progress and safety required cooperation? Based on those case studies, what lessons might be most applicable to cooperation in space, and more specifically, the Moon? In this thesis, I use theoretical and empirical approaches to address these questions. In the theoretical domain, the international relations theories of realism, liberalism, and constructivism will be used to explore how rival and allied countries, plus their agents and industries, have interacted in global commons domains before, which can provide clues about their future actions on the Moon. In the empirical domain, this thesis will use Antarctica and the high seas as analogs for this Moon and take clues from their histories to anticipate what may occur on Earth’s largest satellite. I conclude with predictive and prescriptive answers to the earlier questions, recognizing that realism is most likely to explain how actors will behave in space, but suggesting how liberalism might be a more useful framework. The implications of this understanding are significant. They are also timely, since efforts around establishing Moon stations are expected to accelerate in the coming decade.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".