An Assessment of China's Anti-Satellite and Space Warfare Programs, Policies and Doctrines
Bibliographic record
Abstract
The first two parts of this study present the results of a survey of Chinese writings that discovered 30 proposals that China should acquire several types of anti satellite weapons. Many foreign observers have mistakenly claimed that China is a pacifistic nation and has no interest such weapons. The Director of the US National Reconnaissance Office Donald Kerr confirmed a Chinese laser had illuminated a US satellite in 2006. These skeptical observers dismissed that laser incident, but then appeared to be stunned by the reported Chinese destruction of a satellite January 11, 2007. China declined to confirm the event, but many foreign governments immediately protested,1 including Japan, South Korea, Australia, Canada and Britain, while Russia's defense minister suggested the report may not be fully accurate. A Chinese foreign ministry spokesman, while declining to confirm the incident, said other countries should not be alarmed. A US NSC spokesman said China fired a missile to destroy an orbiting weather satellite, making it the third country after the United States and the former Soviet Union to shoot down anything in space. If confirmed, the test would mean China could now theoretically shoot down spy satellites operated by other nations.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".