Development of the Natural Resources of the Moon and Other Celestial Bodies: Economic and Legal Aspects
Bibliographic record
Abstract
The path of gradual commercialization of current space applications, such as launch services, satellite communication services, direct broadcasting services, satellite remote sensing and navigation services, and satellite weather monitoring services, will most likely be followed by future activities of use of space resources. Ventures, like mining the natural resources of the Moon and asteroids, are likely to become technologically feasible in the near future. The question is what would be the most appropriate approach to address the future needs of exploitation of space resources: should it remain the exclusive province of state governments; should the private sector take over such space activities; or should a public-private partnership type of venture be encouraged? As state governments are becoming constrained by budget deficits, an increased reliance on private sector involvement in space activities involving the extraction and use of space resources is to be expected. When deciding whether to invest in commercial ventures of resource use exploitation, any potential private investor will be faced with the issues of economic costs, risks, and perceived regulatory barriers. This study argues that the perceived regulatory barriers, i.e., the licensing requirement, the “common heritage of mankind” principle of international space law, and protection of intellectual property rights, are not obstacles to economic development. Governments should provide both policy and regulatory incentives for private sector participation in the area of space natural resource use by funding basic research and development and by sponsoring liability insurance for private ventures among other incentives.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".