Island platforms and the hyper-terrestrialisation of Singapore’s smart city-state
Bibliographic record
Abstract
This paper foregrounds the importance of underlying territorial formations in realising a vision of the smart city. It argues that as a political technology of the state, territory should be understood as a platform upon which data works and the smart city unfolds. In this view, island territories – of which bordered city-states like Singapore provide paradigmatic examples – provide an integral, yet hitherto unexplored, component in the realisation of urban ‘smartness’. We illustrate these theoretical arguments through an analysis of how the territorial constraints that characterise Singapore’s island platform enable the state to accurately and effectively realise its vision of a smart city. As both an island city and a city-state, Singapore’s territory is a political technology that is just as important in realising the state’s vision of smartness as the adoption of digital technologies and the management of data. Drawing on 27 interviews with 31 architects of Singapore’s Smart Nation, we empirically explore the integration of data, city and territory through the platform; the ‘hardness’ of data and the ‘softness’ of the city; and the hyper-terrestrialisation of ‘smartness’ in Singapore. Overall, we demonstrate how the idea of territory as a platform provides a generative counterpoint to critiques of platform urbanism.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".