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Record W2737611064 · doi:10.22215/cria.v3i0.120

The Rising Shi’a Crescent: Iranian Smart Power and Implications for the Middle East, Central Asia, and the Persian Gulf

2016· article· en· W2737611064 on OpenAlexaffvenue
Natalia Paunic

Bibliographic record

VenueCarleton Review of International Affairs · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsCarleton University
Fundersnot available
KeywordsMiddle EastPersianIslamSoft powerIslamic republicDiplomacyAncient historyPower (physics)Political scienceRegional powerSmart powerHard powerEconomyHistoryGeographyDevelopment economicsChinaEconomicsPoliticsLawTheologyArchaeology

Abstract

fetched live from OpenAlex

The circumstances of the American war in Iraq, the Arab Spring, and the international fight against Da’esh have provided an opportunity for the Islamic Republic of Iran to consolidate its power in the Middle East, as foretold by King Abdullah II of Jordan in his coining of the phrase “Shi’a crescent” in 2004. Iran’s successful resurgence can be attributed to its effective use of “smart power,” or the combination of soft and hard power tactics. Iran’s hard power, in the form of Shi’a militias and proxies, is supported with soft power tactics. These include Iran’s export of the ideals of the Revolution and its model of an Islamic Republic, its role as leader of the global Shi’ite community, and its investment in cultural diplomacy across the Middle East as well as its renewed engagement with the West.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.695

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.024
GPT teacher head0.275
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2016
Admission routes2
Has abstractyes

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