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Record W2156869870

Winning the UN War: Strategy for the War on Terrorism

2004· article· en· W2156869870 on OpenAlexvenueno aff
Charles V. Peña

Bibliographic record

VenueJournal of military and strategic studies · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTerrorismAl qaedaPolitical scienceVictoryHomeland securityIdeologyHomelandSpellAdversaryPolitical economySpanish Civil WarAsymmetric warfareLawOrder (exchange)Computer securityPoliticsSociologyBusinessComputer science
DOInot available

Abstract

fetched live from OpenAlex

The war on terrorism is a different kind of war that requires a different strategic approach. The enemy is not a generic evil or even terrorism itself, but the al Qaeda terrorist network and the radical Islamist ideology it is fueling. A winning strategy would consist of three central elements, in ascending order of importance: homeland security against future terrorist attacks, dismantling and degrading the al Qaeda terrorist network, and a foreign policy that does not needlessly create new terrorists. Ultimately, changing U.S. foreign policy to stem the tide of growing anti-American sentiment overseas is the most important element because we cannot build a perfect defense against every potential terrorist attack and we cannot kill each and every al Qaeda terrorist. Although changing U.S. foreign policy may not guarantee victory in the war on terrorism, not changing it will spell certain defeat.

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 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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.067
GPT teacher head0.340
Teacher spread0.273 · 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 teacher head, not a consensus.

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

Citations1
Published2004
Admission routes1
Has abstractyes

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