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Record W3028971191 · doi:10.1080/01436597.2020.1754126

Solving the security–democracy dilemma: the US foreign policy in Tunisia post-9/11

2020· article· en· W3028971191 on OpenAlexaff
Pietro Marzo

Bibliographic record

VenueThird World Quarterly · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsForeign policyDilemmaDemocracySecurity dilemmaAuthoritarianismDemocratizationPolitical sciencePolitical economyRhetorical questionLiberalismScholarshipGrand strategySociologyLawPoliticsEpistemology

Abstract

fetched live from OpenAlex

Scholarly consensus postulates a sharp contrast exists between liberal values and realist interests in US foreign policy in the Middle East and North Africa (MENA) region, which finds its expression in the ‘security–democracy’ dilemma.​ This means the US rhetorical determination to abide by the values of a ‘liberal’ foreign policy is neutralised by the ‘realist’ priority of maintaining US strategic interests, which requires support for friendly authoritarian rulers. Scholarship tends to apply this reasoning indistinctly to the entire region, providing an encompassing framework of analysis for understanding US foreign policy, which is valid across time and space. This study challenges this theoretical assumption and argues that while the US might indeed have a comprehensive regional approach in the MENA, the resulting foreign policy follows country-based trajectories that respond to national specificities and the perceived implications for US strategic interests. Exploring US foreign policy in the MENA after 9/11, the article demonstrates that while the US emphasis on liberalism crumbled when faced with security issues, the US liberal approach to Tunisia unfolded more consistently. Although the US continued formal cooperation with Ben Ali’s regime, it empowered at the same time a coalition of democratic opponents, solving the security–democracy dilemma and positively influencing the Tunisian democratisation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.286
Teacher spread0.268 · 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 designTheoretical or conceptual
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

Citations7
Published2020
Admission routes1
Has abstractyes

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