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Record W1591191663 · doi:10.24908/fg.v8i2.4377

PARTITION AND CONFLICT TRANSFORMATION IN INDIA-PAKISTAN AND CYPRUS

2011· article· en· W1591191663 on OpenAlexvenueno aff
Akisato Suzuki

Bibliographic record

VenueFederal Governance · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsnot available
Fundersnot available
KeywordsPartition (number theory)Ethnic conflictFederalismConflict analysisConflict transformationPolitical scienceHuman settlementPolitical economyGeographyEconomic systemConflict resolutionDevelopment economicsPoliticsEconomicsMathematicsLaw

Abstract

fetched live from OpenAlex

This article argues that partition – a peacebuilding approach in apost-conflict society – can lead to the transformation of intrastate conflict to interstate conflict, thereby providing a helpful insight for further comparison of partition with multi-ethnic settlements such as federalism/powersharing and reconciliation. While advocates of partition maintain that intrastate conflict caused by a security dilemma between ethnic groups can be settled only by partition, this article argues that partition could cause the transformation of conflict rather than settling it. The cases of India-Pakistan and Cyprus provide the empirical evidence. The partition of India and Pakistan transformed intrastate conflict within India into interstate conflict between India and Pakistan including nuclear competition. The partition of Cyprus contributed to interstate conflict between Greece and Turkey. Therefore, this article concludes that the transformation of conflict reduces the value of partition, and that it is necessary to take this point into consideration when partition is compared with alternatives such as federalism/powersharing andreconciliation.

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)0.001
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.006
Scholarly communication0.0040.001
Open science0.0000.004
Research integrity0.0000.001
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 head0.033
GPT teacher head0.288
Teacher spread0.255 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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