Bibliographic record
Abstract
Introduction - Natalie Bormann and Michael Sheehan 1. Unbundling sovereignty, territory and the state in outer space: Two approaches - Jill Stuart 2. Space weapons - Dream, nightmare or reality? - Dave Webb 3. Critical astropolitics: The geopolitics of space control and the transformation of state sovereignty - Raymond Duvall and Jonathan Havercroft 4. The spaces between us: The gendered politics of outer space Penny Griffin 5. The lost dimension: A spatial reading of US weaponisation of space - Natalie Bormann. 6. Haunted dreams: Critical theory, technology and the militarization of space - Columba Peoples 7. The (power) politics of space: the US astropolitical discourse on global dominance in the War on Terror - David Grondin 8. Between blind faith and deep scepticism: The 'weaponisation of space' and the Canadian debate on ballistic missile defence - Miguel de Larrinaga 9. The mice that soar: Smaller states perspectives on space weaponisation - Wade Huntley 10. Profaning the path to the sacred: The militarisation of the European space programme - Michael Sheehan 11. Neo-Realism and the Galileo and GPS Negotiations - Iain Ross Ballantyne Bolton 12. Pol Sci-Fi 101: Lessons from science fiction television for global and outer space politics - Mark D Hamilton
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 | 0.001 | 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 | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.012 |
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".