Constructing a centre on the periphery: urbanization and urban design in the island city of Nuuk, Greenland.
Bibliographic record
Abstract
Both islands and cities are often conceptualized in terms of centre-periphery relationships. Scholarly attempts to nuance popular associations of islands with peripherality and cities with centrality reflect awareness of underlying power relationships. Drawing upon island studies and urban studies knowledge, the case of Nuuk, Greenland, is used to explore how centring and peripheralizing processes play out in an island city. Greenland as a whole came to be regarded as a peripheral region under Danish colonialism, but since the 1950s, Danes and Greenlanders have sought to transform Greenland into its own centre. Nuuk grew into a city and a political, administrative and economic centre relative to Greenland’s small settlements, which came to be seen as central to Greenlandic culture. Nuuk’s rapid growth – dependent on imported Danish designs, materials, technologies, policies and labour – has resulted in an island city of immense contrasts, with monumental modern buildings standing alongside dilapidated 1960s apartment blocks and with strongly differentiated neighbourhoods. Nuuk is both at the centre and on the periphery, enmeshed in power relationships with other Greenlandic settlements and with Denmark. Nuuk is a result of urban design processes that are conditioned by both infrastructural systems and a confluence of spatio-temporal factors.
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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 | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".