Space Enthusiasts, Power, Kinship and Unpredictability: The Human Journey to the Cosmos and Outer Space Ethics
Bibliographic record
Abstract
Humans and their robotic emissaries are launching into space in ever greater numbers. While the technological aspects of these missions are prioritized and ongoing, the social aspects and ramifications of this movement into a new arena of exploration and exploitation leave questions unanswered. How does this look from the perspective of those interested in space, and are people interested in the quest to travel to space or is it a small privileged few whose voices dominate? These lead me to ask through the use of interviews how people feel about space, and if obligations to act ethically override other factors (scientific, financial, cultural as examples) for going there? Will humanity learn lessons from other historical instances of exploration and colonization on Earth that led to genocidal harm as we travel away from it and move into new areas, or perpetuate elitist, nationalistic and/or capitalist paradigms that prioritize the business or country over the human, and sociocultural beliefs? Or can humans work more inclusively, and collaboratively, to include the social sciences as part of this process, and in so doing, continue the work of decolonization? So called “space colonization” is an arena that could benefit from an increase in regulatory oversight and collaboration.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.098 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".