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Record W4407043049 · doi:10.5539/ass.v21n1p1

‘International’ Causes and Domestic Revolutions: Liberal-Nationalist Revolutions of the Early 20th Century; the Case of China and Iran

2025· article· en· W4407043049 on OpenAlexvenueno aff
Seyed Milad Kashefi Pour Dezfuli

Bibliographic record

VenueAsian Social Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsNationalismMainstreamChinaEnthusiasmPolitical economyPoliticsHegemonyIdeologyPolitical scienceInternational relationsInternational communityPeriod (music)Economic historyDevelopment economicsSocial scienceSociologyLawHistoryEconomicsPhilosophy

Abstract

fetched live from OpenAlex

Mainstream schools of International Relations theory, largely, refrain from studying socio-political revolutions in earnest. Their absence has been compensated for by historians, sociologists, and political scientists who have identified the influence of 'international' causes both in the occurrence and outcome of revolutions in the modern period. Revolutions are international events in their origins, ideologies, processes and consequences and affect the logic and rhythm of international systems of their times. On the other hand, the scholarly enthusiasm toward studying revolutions with revisionist agendas in the conduct of international relations ignores a vast array of revolutions throughout modern history were looking to adapt themselves with the established conventions of the international system and its dominant hegemon. This paper will examine the 'international' causes in a wave of liberal-nationalist revolutions that happened in the early 20th century in a number of less developed Asian countries that had been spared outright colonization and formal integration into European colonial empires.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.013
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.301
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractyes

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