Bibliographic record
Abstract
Humanity’s ascent into space began in 1929 when the German Army tested its first rocket, the A-1. But while militaries have always accounted for a large portion of human space activity, their use of the space environment has been constrained by a mutual self-interest in preserving access to it for communications, navigation, reconnaissance, weather forecasting, arms control verification and early warning. In 1962, the ‘Starfish Prime’ nuclear test demonstrated that nuclear explosions in space pose a major and indiscriminate threat to satellites. This prompted the United States and the Soviet Union to negotiate the 1963 Limited Test Ban Treaty, which prohibits nuclear tests in space. This chapter addresses such tensions between the expansion of military capabilities in space and the need to keep space free of direct conflict. The chapter highlights the growing need for a treaty to ban the testing of ‘kinetic’ anti-satellite weapons, i.e. weapons that rely on violent impacts to destroy a satellite and thus create space debris. Although Russia tested such a weapon in November 2021, the very next month the United Nations General Assembly created an ‘Open Ended Working Group on Reducing Space Threats through Norms, Rules and Principles of Responsible Behaviours’. The chapter concludes with an examination of the potentially destabilising effects of an imminent extension of military activities to cis-lunar space, the region between Earth and the Moon, including special Moon–Earth orbits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.250 | 0.179 |
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".