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Record W2121539428 · doi:10.1080/00905992.2013.867934

Introduction to the special section: minority politics and the territoriality principle in Europe

2014· article· en· W2121539428 on OpenAlexaff
Magdalena Dembińska, László Marácz, Márton Tonk

Bibliographic record

VenueNationalities Papers · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTerritorialityPoliticsAmbiguityAutonomyPolitical scienceTerritorial integrityRegionalism (politics)Section (typography)Corporate governanceState (computer science)Theme (computing)Independence (probability theory)Minority rightsPolitical economyLawSociologySovereigntyDemocracy

Abstract

fetched live from OpenAlex

Territorial arrangements for managing interethnic relations within states are far from consensual. Although self-governance for minorities is commonly advocated, international documents are ambiguously formulated. Conflicting pairs of principles, territoriality vs. personality, and self-determination vs. territorial integrity, along with diverging state interests account for this gap. Together, the articles in this special section address the territoriality principle and its hardly operative practice on the ground, with particular attention to European cases. An additional theme reveals itself in the articles: the ambiguity of minority recognition politics. This introductory article briefly presents these two common themes, followed by an outline of three recent proposals discussed especially in Eastern Europe that seek to bypass the controversial territorial autonomy model: cultural rights in municipalities with a “substantial” proportion of minority members; the cultural autonomy model; and European regionalism and multi-level governance.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0010.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0100.002

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 head0.014
GPT teacher head0.276
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations16
Published2014
Admission routes1
Has abstractyes

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