How States Order the World: A Typology of “Core” and “Peripheral” Foreign Policy
Bibliographic record
Abstract
Abstract Every state's foreign policy has to deal with other states, regions, and transnational issues, not all of whom are likely to receive the same level of policy-making interest and attention. States have differing foreign policy priorities, but how do we conceptualize those different priorities? To explain how states order the world and prioritize their foreign policy, I establish an ideal typology of “core” and “peripheral” foreign policy, which categorizes more and less important foreign policy spaces and issues. This typology contributes to foreign policy analysis's “middle-range” theorizing by establishing how and why the determinants, processes, and goals of foreign policy–making in these distinct types differ, and where policy-makers have the greatest ability to influence change in foreign policy. One of the key insights of this research relates to how structure and agency differently influence foreign policy–making: “core” foreign policy tends to be more structurally rigid and obtrusive, allowing less maneuverability for actor agency seeking to change the status quo, while “peripheral” foreign policy is less structurally rigid and obtrusive, allowing for greater actor agency in changing foreign policy direction and priorities. Hence, this typology should aid our understanding and prediction of foreign policy priorities and decisions.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".