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Record W7131657051 · doi:10.32566/ah.2025.3.5

International Economic Sanctions and Their Implications

2025· article· W7131657051 on OpenAlexaff
Jafaar Sakkour

Bibliographic record

VenueActa Humana · 2025
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicEconomic Sanctions and International Relations
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsSanctionsEconomic sanctionsPoliticsInternational relationsState (computer science)Human rightsInternational law

Abstract

fetched live from OpenAlex

This paper examines the use of economic sanctions as a tool of international policy, focusing specifically on their application to Syria. The study reviews key definitions and types of economic sanctions, highlighting their role in influencing state behaviour while avoiding military intervention. Using a case study methodology, this analysis examines the sanctions imposed by the United States, the European Union, and other international actors since 2011. The paper assesses the economic and humanitarian implications of these measures, including substantial declines in national GDP, disruptions to oil production, inflation, food insecurity, and restricted access to essential medicines. The research also considers the ethical and legal debates surrounding sanctions, emphasising their disproportionate impact on civilian populations rather than political elites. The findings indicate that while sanctions aim to achieve strategic political objectives, their implementation often exacerbates humanitarian crises and undermines human rights. The paper concludes that multilateral coordination and targeted, “smart” sanctions are more effective and ethically justified than broad comprehensive measures. A balanced approach that aligns political goals with humanitarian considerations is necessary for a fair and sustainable application of economic sanctions in international relations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.030
GPT teacher head0.256
Teacher spread0.225 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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