The Logic of Strategic Assets: From Oil to AI
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
What resources and technologies are strategic? Policy and theoretical debates often focus on this question, since the “strategic” designation yields valuable resources and elevated attention. The ambiguity of the very concept, however, frustrates these conversations. We offer a theory of when decision makers should designate assets as strategic based on the presence of important rivalrous externalities for which firms or military organizations will not produce socially optimal behavior on their own. We distill three forms of these externalities, which involve cumulative-, infrastructure-, and dependency-strategic logics. Although our framework cannot resolve debates about strategic assets, it provides a theoretically grounded conceptual vocabulary to make these debates more productive. To illustrate the analytic value of our framework for thinking about strategic technologies, we examine the US-Japan technology rivalry in the late 1980s and current policy discussions about artificial intelligence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it