Handbook for New Actors in Space
Bibliographic record
Abstract
Driven by Cold War tensions between the US and the Soviet Union, the space race began almost 60 years ago. Each power was racing to accomplish new feats in space and demonstrate its superiority. In 2017, while much remains the same, much has changed. Space actors comprise a wide variety of national and non-governmental entities comprising diverse rationales, goals, and activities. More than 70 states, commercial companies, and international organizations currently operate more than 1,500 satellites in Earth orbit. Driven largely by the commoditization of space technology and the lowering of barriers to participation, the number of space actors is growing. This broadening of space has both advantages and disadvantages. On the positive side, it is leading to greatly increased technological innovations, lower costs, and greater access to the beneficial capabilities and services offered by satellites. However, the accelerated growth in space activities and the influx of new actors has the potential to exacerbate many of the current threats to the long-term sustainable use of space. These threats include on-orbit crowding, radio-frequency interference, and the chances of an incident in space sparking or escalating geopolitical tensions on Earth. Michael K. Simpson, Ph.D. - Executive Director, Secure World Foundation
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.143 | 0.114 |
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".