Smart City Innovations: The Role of Local and Global Collaborations
Bibliographic record
Abstract
This paper integrates research on smart cities, innovation ecosystems, and networks to examine how collaboration shapes the development of smart city technologies. It addresses a critical gap in the literature by investigating the roles of both local and global partnerships in driving innovation. Drawing on a negative binomial regression analysis of global patent data, the study evaluates the impact of domestic and international collaborations on smart city innovation. Next, to uncover the underlying mechanisms through which these partnerships influence innovation, the paper combines thematic analysis of interviews with network analysis. The findings identify three key pathways through which collaboration fosters innovation: knowledge transfer and adoption, co-development and experimentation, and standardization and scalability. The study underscores the complementary roles of local and global ties—while local collaborations provide the foundation for implementation, global linkages introduce new ideas and practices that enrich local innovation efforts. The paper concludes with policy recommendations for promoting effective multi-level collaboration in smart city development.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".