The perils of realist advocacy and the promise of securitization theory: Revisiting the tragedy of the Iraq War debate
Bibliographic record
Abstract
Why does realist political advocacy for a more limited national security agenda fail? For nearly two decades, realists in general and Stephen Walt and John Mearsheimer in particular have publicly lamented an endemic problem of threat inflation in America, culminating in the unnecessary 2003 Iraq War. This article argues that understanding the failure of realist advocacy requires appreciating its roots in the model of the marketplace of ideas, an ironically liberal model of discourse that downplays questions of power. As a corrective, I argue that securitization theory and its framing of security debates as discursive, competitive and ultimately power-laden processes offers substantive insights into understanding realism’s anaemic interventions. Focusing specifically on the advocacy of Stephen Walt and John Mearsheimer in their opposition to the 2003 Iraq War, I examine how powerful processes involving social identity and collective emotion came to be turned against realists by their neoconservative interlocutors. In the final section, I suggest that a common research agenda among realism and securitization scholarship is needed to explore their joint interest in the statecraft of threat construction in order to produce practical, politically relevant knowledge.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.133 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".