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Record W2103694317

Honourable Mention: On Message: News Media Influence on Military Strategy in Somalia and Iraq

2007· article· en· W2103694317 on OpenAlexvenueno aff
Dan Fitzsimmons

Bibliographic record

VenueJournal of military and strategic studies · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical sciencePower (physics)Foreign policyAutonomyMass mediaPublic relationsMilitary operationPolitical economyMilitary strategyMedia studiesLawSociologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

This paper provides an analysis of the role of the mass media on American strategic policy in the conflicts in Somalia in 1992-1993 and the 2003 Iraq War. It argues that American policy makers have more autonomy in foreign policy decision-making than is frequently perceived by scholars supporting the notion that mass media organizations possess extensive agenda-setting power, commonly know as the “CNN effect.” The arguments of scholars supporting the agenda-setting power of media organizations are discussed and evaluated to determine if US military strategy was affected by the largely negative media presence in either of these conflicts. This paper is unique because it shifts focus away from the field of foreign policy, and specifically the decision making process surrounding engagement and extraction from war zones, and instead focuses the news media’s role in influencing strategic direction of a war. It concludes that the negative tone of the news media did not have a significant effect on military strategy due to the consistent messages delivered by American elites throughout both conflicts, and the inability of news makers to force any noticeable changes to military activities.

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)0.001
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.053
GPT teacher head0.355
Teacher spread0.301 · 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
Published2007
Admission routes1
Has abstractyes

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