Science fiction in military planning—Case allied command transformation and visions of warfare 2036
Bibliographic record
Abstract
Abstract This article focuses on using science fiction for military purposes to anticipate the future of warfare and presents a new tool for creating military science fiction. As technology is a significant driver in the future of warfare, science fiction has increased its popularity for military purposes. Armies and defense organizations have begun utilizing science fiction to anticipate and prepare for future wars. Examples can be found in Canada, the United States, the United Kingdom, France, Australia, and NATO. Even though military sci‐fi is on the rise, there is a lack of a more profound analysis of the sci‐fi narratives of the military and its foundations. Allied Command Transformation's, (NATO's Strategic Warfare Development Command) report called Visions of Warfare 2036 (2016) exhibits an example of military‐based science fiction employed to anticipate and get prepared for the future of warfare. It includes 12 narratives of the future of warfare varying from gene‐manipulated soldiers to AI‐generated warfare. By analyzing the report qualitatively using the Atlas.ti program and manual methods, the basic elements of the stories were identified. One of the findings of the analysis was that the stories were somewhat similar to each other. To create more diverse military science fiction scenarios, a new tool: the Military Science Fiction Scenario Card was created. This tool can be used in practical work when thinking about the war of the future and in particular the role of technology in it. It can also be seen as a new tool in the field of futures research.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".