Neoclassical Realism as a Theory for Correcting Mistakes: What State X Should Do Next Tuesday
Bibliographic record
Abstract
Abstract Neoclassical realism has carved a unique niche by offering a theoretically derived and empirically rich foreign policy analysis framework. Over the years, it has branched out as a theory of mistakes (Type I), a theory of foreign policy (Type II), and a theory of international politics (Type III). This article proposes another challenge to consolidate its offer of a progressive research agenda to position it as a theory for correcting mistakes. The theory of mistakes version differentiates ideal from actual foreign policy. The ideal corresponds to foreign policy that follows the pressures and incentives of the international system; structural realism, the basis for this optimal baseline, is here viewed as a normative theory. If there is a gap between the ideal baseline and the actual outcome, then foreign policy is sub-optimal and therefore costly. According to neoclassical realists, this is the result of the intervention of domestic political processes hijacking foreign policy. It follows that pointing out how to reduce the distorting impact of these domestic variables should help steer foreign policy toward optimality. By identifying the negative consequences that follow from a sub-optimal foreign policy, a theory for correcting mistakes also opens the door to developing prescriptions to manage the inevitable fallout.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".