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Record W6982489641

Institutions vs. divisions : a comparative assessment of conflict levels in unitary and decentralized states (1990-2020)

2024· article· en· W6982489641 on OpenAlexaboutno aff

Bibliographic record

VenueGhent University Academic Bibliography (Ghent University) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationUnitary statePoliticsInternal conflictContext (archaeology)Empirical evidenceState (computer science)Secession
DOInot available

Abstract

fetched live from OpenAlex

Governing multinational states is not a walk in the park. Facing ethno-territorial debates, secessionist pressures, and regional sensitivities, many of these countries are burdened by political conflicts, gridlocks, and instability. As such, their internal diversity is often said to come with a ‘heterogeneity cost’ (Alesina and Spolaore, 1997). Additionally, due to their institutional and historical features, some multinational states are argued to be even more conflict-prone than others. In this respect, many scholars have stressed the importance of -amongst others- the level of decentralization, the number of substates, and the centrifugal or centripetal nature of these states (‘coming together’ or ‘holding together’). This paper seeks to address two questions: (1) Are multinational states indeed more conflict-prone than other states – as is often assumed? (2) Are certain systems indeed more conflict-prone than others? To date, empirical studies on these questions are scarce and mainly preoccupied with indicators of violent conflict (civil war, terrorism) or the survival of states as such (e.g. Brancati, 2009; Cordell and Wolff, 2016; Horowitz, 2000). Meanwhile, very little attention has gone to mundane conflicts between the political parties and actors that govern divided states. Such everyday political clashes deserve our attention too. It is well-known within the field and stressed by consociational theory (Lijphart, 2002, 1969) that the everyday behavior of politicians is key to the stability of divided states. Assessing such behavior is especially important in a context in which rising levels of electoral volatility and party fragmentation put political cooperation under pressure (Siaroff, 2019). This study aims to address this gap. The paper presents a novel, unprecedented large-N dataset on three decades of everyday political conflicts in over 100 national cabinets in 10 countries with varying levels of internal heterogeneity and different institutional systems: Belgium, the Netherlands, Canada, Denmark, France, Germany, Italy, Spain, Switzerland, and the UK (1990-2020). Using a recently developed approach to measuring cabinet conflicts (Vandenberghe, 2022), conflicts at the central level are tracked by hand-coding the Political Data Yearbooks of the authoritative European Journal of Political Research (EJPR). Using this data allows us to compare countries and discern five of their features: the degree of heterogeneity (multinationalism), the federal/unitary nature, their level of decentralization, the number of substates (bipolar/multipolar), and their centrifugal/centripetal nature. Doing so allows us to add both empirically and theoretically to ongoing debates on the so-called paradox of federalism (Erk and Anderson, 2010) and on conflict and conflict-management in multinational states more generally (Coakley, 2009; Wolff and Yakinthou, 2012; Cordell and Wolff, 2016; Keil and Anderson, 2018). Furthermore, the dataset presented here offers great potential for different fields, most notably that on coalition conflicts (Bergman et al., 2021).

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.076
GPT teacher head0.352
Teacher spread0.276 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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