Harnessing the environment for socio-ecological encounters and knowledge spillovers: Lessons from the Greater one-north (GO) Creative city design explorations
Bibliographic record
Abstract
Knowledge cities may be understood as cities that rely primarily on the global knowledge economy such that they attract the creative class in our globalized world. Examples include but are not limited to New York City and San Francisco. As East Asia undergoes rapid urbanisation, more cities, e.g., Shenzhen, are modelled to capture the economic benefits of ‘knowledge spillovers’ (Henderson, 2007). The Knowledge Spillover Theory suggests that innovation is concentrated in some quarters of the city because of informal social exchanges across co-located industries. Business incubators are often introduced to accelerate these processes. For instance, Launchpad@One-north, Singapore, is surrounded by media corporations, a university, a business park, mass transit, and nature areas. The Greater One-north area required spatial planning as more intensive developments were expected to serve future housing along the southern waterfront. Action research-by-design was employed to examine the economy-ecology outcomes from scenarios of varying densities. In this paper, we identify two types of economy-ecology synergies that may be achieved by careful spatial design for socio-ecological encounters. Four scenarios with different orientations toward the economy-ecology dichotomy illustrate these to varying degree. Overall, a high-quality environment is synergistic. Simultaneously centring humans and nature may attract talent and accelerate innovation in knowledge cities.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".