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Record W2132010082 · doi:10.1111/misr.12239

Leadership Styles and Policy Breakdown: Bush and Rumsfeld and the War in Iraq

2015· article· en· W2132010082 on OpenAlexaffabout
David B. MacDonald

Bibliographic record

VenueInternational Studies Review · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPoliticsPolitical scienceLawVietnam WarSpanish Civil WarWar on terror

Abstract

fetched live from OpenAlex

Drawing on European folktales, Isaiah Berlin once divided cognitive approaches between those who see problems like hedgehogs, focusing on one big thing, and foxes, who know many little things. Based on two dozen interviews with senior policy advisors and officials, Stephen Dyson applies this bifurcation to George W. Bush and Donald Rumsfeld, with Bush as the simplistic black and white hedgehog, and Rumsfeld as the complex and sometimes wily fox. Dyson's work privileges the study of individual leaders in the study of international politics and, contra realism, he argues that it is impossible to understand the war in Iraq without considering the personalities of the President and his Secretary of Defense. This study complements Dyson's earlier work on conflict and leaders, including Tony Blair, Margaret Thatcher, Adolf Hitler, and Saddam Hussein. The basic message of the book is repeated throughout, and most cogently articulated at the end: “George W. Bush's worldview was based upon clear-cut principles, instinctive, non-reflective decision making, a strong moralistic bent, and a propensity for risk-taking based upon a belief in the history-making potential of strong leadership… [In contrast] Rumsfeld saw the world as filled with nuance, complexity and contingency. He was obsessed with the unknowable, and deeply suspicious of grand doctrines or ambitious policy schemes” (p. 125).

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0030.004
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.209
GPT teacher head0.439
Teacher spread0.230 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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