The Response of Local-Level Public Authority to the Digital Transformation in Three Contexts
Bibliographic record
Abstract
The dominance of the rational planning paradigm has prompted the urban digital transformation. This transformation can be seen in the computational advancements that unlock faster and more detailed representations of urban ecosystems; the public and private investments in smart city research and development; and the urgent demand for more robust and responsive infrastructure management systems at the local government level. Local governments, especially municipalities, shoulder a tremendous burden in adapting to an increasingly digital—and supposedly more rational—urban landscape. The idea is that ubiquitous digital infrastructure systems that enable more extensive data collection and analysis, will improve governance practices and redress past socioeconomic, political, and environmental harms and prevent future ones. Through studies of the traditional trajectory of technology adoption, corporate re-imaginings of urban life, and the challenges of cybersecurity and governing ubiquitous digital infrastructure at scale, this dissertation explores how local-level public authorities negotiate and practice digital infrastructure governance. The overall finding from this research is that local governments determine the efficacy of urban digital infrastructure because of their remit and administrative capacity. While developers and entrepreneurs argue that government incompetence or a lack of advanced technology thwarts the realization of their utopias, the oft-derided bureaucracy reveals—and protects against—threats to the erosion of public authority as guarantor of the public interest. This research finds that this emergent dual-function will be a critical attenuating force against technology developers asserting their perceived entitlement to public governance. Each chapter studies rational planning as the prevailing ideology guiding urban governance and shows how cities respond to external forces that leverage rational planning to drive the urban digital transformation. Chapter 1 demonstrates how advancements in traditional analytical tools in research and practice can be used by local departments of transportation (DOT), a group whose authority to implement traffic management strategies has historically exceeded their capacity to analyze network dynamics. Through conducting a simulation of the City of San Jose's Safer Streets strategy, I find that local DOTs can virtually implement and analyze the neighborhood-level and network-level impacts of localized traffic management strategies; two key results from the simulation are that imposing the 20 mph speed limit cap on residential streets in San Jose EPCs leads to a 39% reduction in passthrough traffic on those streets, and in some cases, the diverted vehicles contribute to a 76% increase in traffic on streets in the surrounding network. Chapter 2 uses a case study of the cancelled Quayside smart city project in Toronto to explore the influence of public authority on how smart city entrepreneurs develop and implement their initiatives. There, I find that public authority can block entrepreneurial rational planning initiatives, namely, smart cities, whose development is heavily sanctioned by technology firms. This finding challenges the prevailing notion in smart city scholarship that local governments are at a governance authority disadvantage relative to the private sector. Chapter 3 shows that the progressive rollout of rational planning tools expands a costly and unbounded infrastructure risk profile that local governments cannot manage alone. Through a study of the sources of transaction costs of municipal cyber risk management, I present the procedural requirements that cities must satisfy in order to address threats to their cyberphysical systems. I find that municipalities are ill-positioned to independently manage cyber risk, meaning that they will incur transaction costs in establishing and maintaining the governmental and business relationships needed to address cybersecurity. Accounting for these requirements provides local governments a more complete picture of the required expenditures for technology adoption and deployment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".