Information Sharing as a Dimension of Smartness: Understanding Benefits and Challenges in Two Megacities
Bibliographic record
Abstract
Cities around the world are facing increasingly complex problems. These problems frequently require collaboration and information sharing across agency boundaries. In our view, information sharing can be seen as an important dimension of what is recently being called smartness in cities and enables the ability to improve decision making and day-to-day operations in urban settings. Unfortunately, what many city managers are learning is that there are important challenges to sharing information both within their city and with others. Based on nonemergency service integration initiatives in New York City and Mexico City, this article examines important benefits from and challenges to information sharing in the context of what the participants characterize as smart city initiatives, particularly in large metropolitan areas. The research question guiding this study is as follows: To what extent do previous findings about information sharing hold in the context of city initiatives, particularly in megacities? The results provide evidence on the importance of some specific characteristics of cities and megalopolises and how they affect benefits and challenges of information sharing. For instance, cities seem to have more managerial flexibility than other jurisdictions such as state governments. In addition, megalopolises have most of the necessary technical skills and financial resources needed for information sharing and, therefore, these challenges are not as relevant as in other local governments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".