Public Support for Emerging Military Technology Development in “Middle” Powers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emerging technologies, including artificial intelligence, robots, and nanotechnology, have the potential to fundamentally alter military capabilities and strategies. While there is a growing interest in public attitudes toward such technologies, existing work remains largely focused on the conditions under which citizens, especially in the United States, support their use in combat. In contrast to prior work, we focus on public preferences for military technology development in “middle” powers. Unlike “great” powers, like the United States, “middle” power countries operate in a much different strategic environment. This project thus asks: how do the specific features of emerging military technologies affect public support for their development? We argue that citizens’ multidimensional preferences toward the development of new military technology encompass particular strategic (e.g., technology transfer to allies) and consequentialist (e.g., potential harm to civilians) concerns. To empirically test our argument, we rely on a conjoint experiment embedded in population surveys in Canada and Japan. Our findings provide original causal evidence of how different strategic and consequentialist dimensions of military technology development shape citizens’ preferences.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.009 |
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 it