Bibliographic record
Abstract
This article criticises the traditional realist distinction between “high politics” (sovereignty and security matters) and “low politics” (economics and other “less important” state activities) on several grounds. First, it ignores the economic underpinnings of military power and national security. Second, it overestimates the independence states have both from the international economy and from domestic political opposition when mobilizing economic resources in support of security objectives. Finally, it glosses over the potential for states to achieve national security objectives in an interdependent world economy by using economic instruments, such as economic sanctions and economic incentives. This article, therefore, makes the case for treating the political economy of national security as a distinct subfield of security studies for both teaching and research purposes. It identifies a unique set of “political economy” issues that have a direct bearing on national security calculations. It reviews both the classical geopolitics literature and a growing literature by contemporary international relations scholars that address these issues in an effort to bridge the chasm between political economy and security. Finally, it suggests avenues of further research to flesh out the conditions under which domestic and international economic factors affect the pursuit of national security.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".