The Empire Strikes Back: Comparing US and China’s Structural Power in Outer Space
Bibliographic record
Abstract
Abstract This article assesses the structural power of the United States and China in the field of space governance. While much of the literature on space power focuses on their technologies and capabilities, we take a complementary approach and explore their capacity to shape the regulatory landscape. Possessing structural power has far-reaching implications for global power projection as well as for various industries, such as telecommunications, transportation, and remote sensing. To assess structural power, we gathered and analyzed three types of data: a dataset featuring 1,709 space organizations, a second dataset comprising 1,764 international space arrangements connecting them, and insights from fifty-two interviews with key space actors. Our findings indicate that the United States holds significant structural power thanks to its thriving commercial space sector and extensive international network. This has enabled the global diffusion of its preferred norms while simultaneously constraining China’s space cooperation network. Despite its remarkable technological capabilities, China has not been able to translate them into substantial global structural power. To encourage further exploration in this domain, we make available our original dataset of 1,764 space arrangements, including 970 in full-text format, inviting fellow researchers to investigate other facets of outer space governance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".