Bibliographic record
Abstract
The smart city concept recently (ca. 2010) emerged as a corporate-led system-as-a-service (SaaS) tool to meet city needs of accessibility and efficiency. I looked at three Western cities—Reykjavík, San José, and Toronto—to discover what it meant for city managers to meet municipal needs by embracing smart initiatives. Senior-level city managers, consultants, and technologists invoked vocabularies of smartness and innovation, adopting Internet of Things (IoT) and artificial intelligence (AI) as tools to facilitate human resource and service efficiency needs. I found persistent ambiguity in how city managers described and measured outcomes for city smartness. I also found stakeholders used smartness to participate in global knowledge sharing coalitions with public and private entities, amplifying negotiation potential, and producing values of prestige around novel technological innovation. In so doing, public and private stakeholders formed individual and organizational identities around technological innovation, creating invisible tensions between human resource and technology investments, characterized by celebration of innovation work to the detriment of maintenance labors. My findings inform ongoing scholarship by explaining how smart city technologists sold a discourse of innovation that was not entirely compatible with how cities bureaucratically functioned. Such distinction is important to communicate to scholarly audiences unfamiliar with techno-fetishisms, but familiar with urban management critiques. Moreover, my study opens paths to understanding how private interests influence municipal management through more obscured means.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".