MétaCan
Menu
Back to cohort
Record W4399099803 · doi:10.1111/nana.13030

Fighting over nation or state: States, communal demography, and the type of ethnic civil war

2024· article· en· W4399099803 on OpenAlexafffund
Matthew Lange, Tay Jeong

Bibliographic record

VenueNations and Nationalism · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEthnic groupState (computer science)Spanish Civil WarPolitical scienceDemographySociologyLaw

Abstract

fetched live from OpenAlex

Abstract We recognise nationalist and centre‐seeking ethnic civil wars as distinct types of conflict and draw on key ideas from political sociology to make hypotheses about the causes of each. First, we argue that the character of states shapes antistate actors in ways that channel ethnic conflict in different ways, with pluralist states promoting nationalist warfare but integrative states contributing to centre‐seeking civil war. Second, we propose that the relative power of communities affects the type of ethnic civil war, arguing that centre‐seeking civil war is most common in situations of communal multipolarity whereas nationalist civil war is concentrated in regions with asymmetric power relations. And because historical statehood promotes elements of pluralist states and asymmetric communal power relations, we hypothesise that the risk of nationalist civil war is high in places with large and longstanding states. To test these hypotheses, we use ethnic fractionalisation to measure configurations of communal power and the state antiquity index to measure level of historical statehood, create a variable measuring the extent to which colonial states were pluralist, and run panel analyses of the odds of civil war onset. With one possible exception, the findings support our hypotheses.

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)0.001
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 score0.763
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.047
GPT teacher head0.365
Teacher spread0.318 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueNations and NationalismSame topicPolitical Conflict and GovernanceFrench-language works237,207