Seeking the 'Smart' in Cities: Managing the process of Innovating with IT
Bibliographic record
Abstract
A smart city can be defined as a city seeking to address public issues via information technology solutions on the basis of a multi-stakeholder, municipally based partnership. Core to the smart city agenda is realizing a new innovation strategy for municipal governance based on high levels of cooperation among stakeholders to improve the efficiency and quality of public service delivery. Governments can enact an integrated digital platform to support high levels of cooperation among stakeholders with process management, and thereby align cooperative activities with public priorities. Drawing on punctuated equilibrium theory, we examine what know-how enables some city managers to manage implementation of an integrated digital platform for innovating with IT, and what know-how is lacking in city governments that fail to do so. We report evidence from case studies in eleven city governments to identify what know-how city managers require for the competence to manage such an implementation.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".