The relational co-production of “success” and “failure,” or the politics of anxiety of exporting urban “models” elsewhere
Bibliographic record
Abstract
This paper critically examines the case of the much-vaunted Singapore “model” and its export via the Sino-Singapore Tianjin Eco-city (SSTEC), a megaproject jointly developed by the Singaporean and Chinese states in northeastern China. It revolves around the central question of why, for some Singaporean officials, this export was thought to have “failed” in spite of the model’s acclaimed success globally. To address this, the paper historicizes the Singapore model, tracing undercurrents of (geo)political existentialism through Singaporean state meta-narratives that are enacted through thehistorical politics of anxiety and the practitioner politics of anxiety. It argues that categories of policy “success” and “failure” are relationally co-produced through a politics of anxiety, wherein their stakes are amplified in ways distinctive to small postcolonial city-states. Collectively, the paper emphasizes the enduring significance of (inter)state actors and structures for transnational urban policy mobilization and the limits to assumptions of post-failure policy learning.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.060 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".