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Record W2079486721 · doi:10.1093/publius/pjv016

Negotiating Territorial Change in Multinational States: Party Preferences, Negotiating Power and the Role of the Negotiation Mode

2015· article· en· W2079486721 on OpenAlexaboutno aff
Bettina Petersohn, Nathalie Behnke, Eva Maria Rhode

Bibliographic record

VenuePublius The Journal of Federalism · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationMultinational corporationAutonomyPower (physics)Mode (computer interface)Political scienceEconomic geographySpace (punctuation)Economic systemPolitical economySociologyBusinessEconomicsLawComputer science

Abstract

fetched live from OpenAlex

In this article, we offer an explanation for varying patterns of territorial reforms aimed at accommodating claims for more substate autonomy in multinational states. We argue that the interaction between preferences of state-wide and non-statewide parties, their negotiation power and the negotiation mode accounts for specific patterns of territorial change. Analytically, we advance existing research in two ways: First, by analyzing territorial change in a two-dimensional space (vertical and horizontal), we pay explicit attention to jurisdictional heterogeneity between substates. Second, by applying an actor-centered institutionalist approach, we highlight the strategic potential of actors within the institutional setting. The comparative analysis of thirteen processes of territorial change in four multinational Western democracies—Canada, Belgium, Spain, and the UK—reveals, first, certain conditional effects of the independent variables on specific patterns of territorial change and, second, how the negotiation mode impacts on a party’s negotiation power.

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)0.006
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.297
Teacher spread0.266 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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Same venuePublius The Journal of FederalismSame topicPolitical Systems and GovernanceFrench-language works237,207