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Record W3127749483 · doi:10.24043/isj.146

Electoral politics, party performance, and governance in Greenland: Parties, personalities, and cleavages in an autonomous subnational island jurisdiction

2021· article· en· W3127749483 on OpenAlexvenueno aff
Yi Zhang, Xinyuan Wei, Adam Grydehøj

Bibliographic record

VenueIsland Studies Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsJurisdictionPolitical economyPolitical scienceCorporate governanceElectoral geographyHome ruleAutonomyPublic administrationLawSociologyEconomics

Abstract

fetched live from OpenAlex

Greenland is a strongly autonomous subnational island jurisdiction (SNIJ) within the Kingdom of Denmark. This paper takes its point of departure in studies of politics in small island territories to ask to what extent Greenland matches findings from other small island states and SNIJs in terms of personalisation of politics, party performance, and political cleavages that do not follow left-right divides. Even though Greenland possesses a strongly multiparty system, supported by elections involving party-list proportional representation within a single multimember constituency, a single political party, Siumut, has led the government for all but a brief period since the advent of Greenlandic autonomy in 1979. By considering Greenland’s political ecosystem, spatially and personally conditioned aspects of voter behaviour, and coalition-building processes, paying particular attention to the 24 April 2018 parliamentary elections, we argue that it is inappropriate to study Greenland as a monolithic political unit or to draw oversimplified analogies with party politics from large state Western liberal democracies. Instead, Greenlandic politics must be understood in relation to the island territory’s particular historical, geographical, and societal characteristics as well as its electoral system.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.172
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.028
GPT teacher head0.296
Teacher spread0.268 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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