The citizen and the smart city: a global comparison of institutional logics
Bibliographic record
Abstract
Critical urbanists’ placement of smart city initiatives in Western Europe or North America mostly on “low” rungs of participatory ladders or scaffolds does not mean that smart urban development connotes democracy-eroding neoliberalization everywhere. This article offers a more globally variegated and dynamic understanding of the relationship between citizens and smart cities. We apply an institutional logics frame to the citizen-smart city nexus in seven cities spanning three world regions, in each case considering the interplay of citizen-centric logics with techno-innovation-oriented and bureaucratic/managerial logics. Appreciation of contextually specific institutional orders helps to explain why similar initiatives and intentions yield different outcomes across time and space, but the interplay of competing logics also enables a reworking of prevailing orders and possibilities for change. Bringing multiple cases and associated dynamics into comparative conversation reveals similarities and differences that would not have been expected from a priori classifications based on geographical region or mode of governance, affording cross-case conceptualization of civil servant “proxy citizenship” and “palimpsests” of social scripts. The article provides empirical, methodological and conceptual resources not only for understanding how prevailing institutional logics variously enable or foreclose citizen action in smart city development, but also for building contextually-attuned propositional agendas for change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".