Electoral politics, party performance, and governance in Greenland: Parties, personalities, and cleavages in an autonomous subnational island jurisdiction
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".