Accidental and ideal island cities: islanding processes and urban design in Belize City and the urban archipelagos of Europe
Bibliographic record
Abstract
This paper explores processes of islanding, de-islanding, and re-islanding in the context of island cities. Although today popularly associated with rural, peripheral, and isolated landscapes, concepts of the ideal city coincided in Medieval and Renaissance Europe with emergent notions of the ideal island. Major European cities such as Amsterdam, Paris, and Copenhagen were established on densely urbanized small islands. In accordance with dominant political and military philosophies, they were subsequently developed through comprehensive urban design into vast yet coherent urban archipelagos. In contrast, the contemporary development of Belize City as an island city took place through piecemeal land reclamation, absent comprehensive urban design, resulting in a very different kind of urban archipelago. Neither the coherent European island cities nor Belize City are generally regarded as islands today, yet their histories of urban development evidence the impact of spatial attributes of islandness on urban form. Comprehensive urban design efforts are currently directed at land reclamation and waterfront revitalization in Belize City, aiming to restore Belize City’s island city status. We argue that cities can be islanded, de-islanded, and re-islanded in the minds of their inhabitants and that it is necessary to recognize the connection between ideal islands and ideal cities in order to understand the islanding process in full.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".