NASA’s Artemis Lunar Program: Hybrid Venture, Complex Infrastructure Regime and Governance Crisis
Bibliographic record
Abstract
Launched in 2020, the Artemis Accords are the most ambitious and collaborative space program to send humans to the Moon and beyond. Spearheaded by NASA, this space exploration project involves a growing number of international partners who are actively contributing to the development of lunar infrastructures at various stages. While the legal issues of a facility on the Moon have been largely addressed, this paper aims to underscore the complexity and scope of the social and technical organization of the Artemis program. To this end, Australia, Canada, Japan, and the United Kingdom, which are strategically closest to the United States are used as case studies to grasp the full implications of a program that is first and foremost the expression of a power projection. This article also argues that U.S. domestic political and budgetary whims render the project’s progress uncertain, exposing the discrepancies inherent in a multinational venture. In the context of U.S.-China systemic rivalry, the implementation of this complex infrastructure regime, involving cutting-edge technology, highlights the interdependencies that the program induces, and the limitations of this hybrid model with its centralized organization and deconcentrated architecture.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".