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Record W2126458616 · doi:10.1093/cjip/poq006

World Order Debates in the Twentieth Century: Through the Eyes of the Two-level Game and the Second Image (Reversed)

2010· article· en· W2126458616 on OpenAlexaboutno aff
Takashi Inoguchi

Bibliographic record

VenueThe Chinese Journal of International Politics · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsnot available
Fundersnot available
KeywordsDialecticOrder (exchange)State (computer science)Competition (biology)Quarter (Canadian coin)Political scienceInternational relationsPolitical economyWorld orderSociologyHistoryLawEpistemologyComputer scienceEconomicsPoliticsPhilosophy

Abstract

fetched live from OpenAlex

This article presents a somewhat unconventional grand framework under which to understand world order, in particular the dialectic framework of international relations. By dialectics I mean approaches to world order, with emphasis on interactions among competing forces within international relations and domestic competition. My objective in applying the two-level game and the second image game and second image reversed to the state strategy of leading powers is to examine and analyze the long-term evolution of world order in the extended 20th century period 1890–2025. My aim is to enrich the existing picture of this evolution in international relations in the last century. More specifically, by focusing on the leading powers within different timeframes of this extended century—Britain in the 19th century, and the United States for the best part of the 20th century, especially the last quarter, and at the dawn of the 21st century—I present a broadly gauged picture of leading powers who, frustrated and challenged by dissidents at home and abroad (sometimes called have-nots), respond by modifying their state strategy by accommodating, placating, and/or suppressing dissident activities in efforts to prolong their leadership status.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.315
Teacher spread0.301 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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