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Record W2776646002 · doi:10.1108/jacpr-10-2017-0329

Political geography of Turkey’s intervention in Syria: underlying causes and consequences (2011-2016)

2017· article· en· W2776646002 on OpenAlexaff
Efe Can Gürcan

Bibliographic record

VenueJournal of Aggression Conflict and Peace Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTurkey's Politics and Society
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGeopoliticsHegemonyContext (archaeology)Intervention (counseling)PoliticsSectarianismPolitical scienceAutonomyOriginalityMiddle EastPolitical economyDevelopment economicsEconomyGeographySociologyLawEconomics

Abstract

fetched live from OpenAlex

Purpose What are the causes and consequences of Turkey’s intervention in Syria? The purpose of this paper is to explore this question by focusing on the time frame from 2011 to 2016, i.e. prior to Turkey’s strategic U-turn from uncompromising enmity toward Russia and Iran. Design/methodology/approach Process tracing is used as the main methodological guideline. Findings Turkey’s intervention in Syria has been driven by a mutually reinforcing interaction of geopolitical, geo-economic and geo-cultural factors. Turkey’s neo-Ottomanist geo-strategy has been militarized in the context of the Arab Spring, perceived decline of US hegemony, increasing Kurdish autonomy and Adalet ve Kalkınma Partisi’s (AKP) electoral setbacks. Second, Turkey’s intervention has been triggered by the converging motivations for energy security, easily gained profits from the black energy market and economic integration with Arab-Gulf countries in the face of a stagnating Western capitalism. A third set of factors speaks to the AKP’s instrumental use of Sunni sectarianism and Kurdish ethnopolitics. Originality/value The research aim is to provide a systematic and multi-causal explanation of Turkey’s involvement in Syria.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.203
GPT teacher head0.498
Teacher spread0.294 · 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 designObservational
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

Citations11
Published2017
Admission routes1
Has abstractyes

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