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Record W2505784817 · doi:10.1017/cbo9781139096836.006

Neoclassical realism and the study of regional order

2012· book-chapter· en· W2505784817 on OpenAlexaff
Jeffrey W. Taliaferro

Bibliographic record

VenueCambridge University Press eBooks · 2012
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsMcGill University
Fundersnot available
KeywordsScope (computer science)Economic geographyOrder (exchange)GeographyPolitical scienceCluster (spacecraft)Set (abstract data type)Regional scienceRealismPolitical economyEconomic systemSociologyEpistemologyComputer scienceEconomicsPhilosophy

Abstract

fetched live from OpenAlex

T. V. Paul observes in this volume's introduction that regional transformation – the conditions under which some geographically delimited regions are more likely to evolve toward greater cooperation and peaceful relations among their constituent states, while other regions remain trapped or degenerate into enduring rivalries and endemic interstate and intrastate war – is "of the utmost importance for crafting appropriate policy initiatives." He defines a region as "a cluster of states that are proximate to each other and are interconnected in spatial, cultural, and ideational terms in a significant and distinguishable manner." Regions, therefore, are a defined subsystem within a broader international (or interstate) system. According to Robert Jervis, we are dealing with a system when there are (a) a set of units or elements interconnected so that changes in some elements or their relationships produce changes in other parts of the system, and (b) the entire system exhibits properties and behaviors that are different from those of the constituent parts. Further, as Barry Buzan points out, "regions" are recent historical phenomena. The existence of a single international system of a sufficient geographic scope and interaction capacity to comprise multiple regions only dates to the eighteenth and the nineteenth centuries.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.990
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.039
GPT teacher head0.259
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations13
Published2012
Admission routes1
Has abstractyes

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