Resolving Conflicting Emotions: Obama's Quandaries on the Red Line and the Fight against ISIS
Bibliographic record
Abstract
Abstract The study of emotions in foreign policymaking has emphasized dominant discrete emotions and how they each lead to specific action tendencies. Scholars often focus on one emotion to explain decisions and have an additive view of emotions. This article argues that decision-makers often feel conflicting emotions and that emotions are not simply additive. What are conflicting emotions’ consequences for foreign policymaking? How are these conflicts resolved? The cases of President Obama's response to the Syrian chemical weapon attack in 2013 and the rise of ISIS in 2014 provide an occasion to study these questions on major security issues surrounding military intervention. This article argues that when decision-makers feel conflicted emotions their anxiety level rises, and that they are likely to attempt to gain time through procrastination, to resolve their conflict by focusing their attention on new developments, and to seek support to bolster confidence in their decision.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".