To the farm, Mars, and beyond: Technologies for growing food in space, the future of long-duration space missions, and earth implications in English news media coverage
Bibliographic record
Abstract
The climate crisis, natural resource exploitation, and concerns around how to feed a growing world population have resulted in a growing chorus identifying the need for a Plan B. For some, this Plan B entails preparing for long-duration space missions and the development of human settlement on Mars. To plan for long-duration space missions, the development of food production technologies that can withstand extreme conditions such as poor soil, lack of gravity, and radiation are increasingly prioritized. These technologies may include genetic engineering, digital agriculture, 3D bioprinting, synthetically grown meat and more. Government and corporate proponents of long-duration space missions—NASA and SpaceX, among others—are actively funding agricultural research in space. They argue that the technologies developed for space will have positive implications beyond Mars—directly benefitting Earth and its inhabitants. This paper demonstrates that news reporting on the technology has been overall uncritical. Media narratives surrounding issues of food growth in space have not been studied. This study analyzes how English news media coverage ( n = 170) from 67 publications report the feasibility of long-duration space missions, human settlements, and high-tech agricultural technologies. We provide a cross-section of the types of agricultural technologies being covered, the key organizations and actors in the field, and a critical analysis of media narratives. Using mixed methods content and discourse analysis, this study finds that the news media publications overwhelmingly portray long-duration space missions as both inevitable and a positive good for humanity. Without critically assessing the societal implications of food technologies for long-duration space missions vis-à-vis their benefits on Earth, we risk glossing over systemic and structural inequalities in the food system.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".