Space citizen: The gap between you, me and the governance of space
Bibliographic record
Abstract
Existing space governance institutions and mechanisms are built on a twentieth-century reality, when states were the main actors and the main, if only, subjects of international law. While international space law and multilateral institutions are facing pressures in the twenty-first-century "spacescape," they should not be disregarded or replaced entirely. The framework they provide is an important foundation. Rather, what is needed is an expansive lens to work toward new approaches to the wicked problems of space governance, including safety, security and sustainability. The number and type of space actors, the ever-increasing nature of human activity in space, the dual-use nature of most space services and the vulnerability of civilians in the event of a loss of space-based services: all of these factors create wicked complexities. An expansive cognitive approach is proposed: that of the individual "space citizen." Since we are all dependent on space-based technologies, we all have a vested interest in the good governance of the space environment and our impact upon it. Existing notions of the "global citizen" and "planetary citizen" should be expanded to the "space citizen," so that we can activate our own individual participation in new governance approaches that are multi-stakeholder, muti-domain, inclusive and intergenerational.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".