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Record W4288445157 · doi:10.1093/fpa/orac016

Resolving Conflicting Emotions: Obama's Quandaries on the Red Line and the Fight against ISIS

2022· article· en· W4288445157 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueForeign Policy Analysis · 2022
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSocial psychologyAction (physics)Political sciencePsychologyIntervention (counseling)ProcrastinationPolitical economyAnxietyPositive economicsSociologyEconomics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.041
GPT teacher head0.339
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it