Outer Space Mining: Exploring Techno-Utopianism in a Time of Climate Crisis
Bibliographic record
Abstract
Outer space holds a special place in the geographical imagination of techno-utopianism. At a time of climate crisis, the mining of celestial bodies, including asteroids, is cast as a possible “tech-driven” response to the need for “green transition” mineral resources in a context of rising geopolitical tensions and concerns over terrestrial extraction. Although still a long way from commercial-scale implementation, outer space mining no longer appears as far-fetched science fiction within the context of a booming “New Space” industry and privatization of celestial commons. Drawing from a growing body of research and critiques of responsible mineral sourcing, we explore some of the legal, political, ethical, and environmental dimensions of outer space mining, and compare them with land-based and deep-sea terrestrial mining. We then point to key areas for further geographical and social sciences enquiries into outer space extractive frontiers, including the uneven distribution of space mining wealth, the impacts on terrestrial mining communities in the Global South, and the reconceptualization of the mining enclave.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".