Competitiveness AND Sustainability: Can ‘Smart City Regionalism’ Square the Circle?
Bibliographic record
Abstract
Increasingly, the widely established, globalisation-driven agenda of economic competitiveness meets a growing concern with sustainability. Yet, the practical and conceptual co-existence—or fusion—of these two agendas is not always easy. This includes finding and operationalising the ‘right’ scale of governance, an important question for the pursuit of the distinctly transscalar nature of these two policy fields. ‘New regionalism’ has increasingly been discussed as a pragmatic way of tackling the variable spatialities associated with these policy fields and their changing articulation. This paper introduces ‘smart (new) city-regionalism’, derived from the principles of smart growth and new regionalism, as a policy-shaping mechanism and analytical framework. It brings together the rationales, agreed principles and legitimacies of publicly negotiated polity with collaborative, network-based and policy-driven spatiality. The notion of ‘smartness’, as suggested here as central feature, goes beyond the implicit meaning of ‘smart’ as in ‘smart growth’. When introduced in the later 1990s the term embraced a focus on planning and transport. Since then, the adjective ‘smart’ has become used ever more widely, advocating innovativeness, participation, collaboration and co-ordination. The resulting ‘smart city regionalism’ is circumscribed by the interface between the sectorality and territoriality of policy-making processes. Using the examples of Vancouver and Seattle, the paper looks at the effects of the resulting specific local conditions on adopting ‘smartness’ in the scalar positioning of policy-making.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".