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Record W4390410470 · doi:10.1177/10780874231222243

Examining the Smart City Generational Model: Conceptualizations, Implementations, and Infrastructure Canada's Smart City Challenge

2023· article· en· W4390410470 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueUrban Affairs Review · 2023
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsSmart cityConceptualizationModernization theoryGovernment (linguistics)VendorImplementationBusinessPublic relationsSociologyPublic administrationEconomic growthPolitical scienceMarketingComputer scienceComputer securityInternet of ThingsEconomics

Abstract

fetched live from OpenAlex

Cohen's Smart City Generational model has been the basis of understanding for the evolution of the Smart Cities movement. However, how does this model align with practitioners’ conceptualization of the term? Our research focuses on Infrastructure Canada's Smart City Challenge (SCC). Through 14 primary interviews and 20 finalist applications, this research reveals that practitioners overwhelmingly understand Smart City building as a government-driven, data-centric endeavor (Smart City 2.0), as opposed to being about vendor transactions (Smart City 1.0), resident engagement (Smart City 3.0), or community co-creation (Smart City 4.0), where the specific technology is of secondary importance to project objectives. We conclude that, rather than moving through distinct generations, the smart cities movement should be understood as a gradual process of municipal public administration modernization as local governments are becoming increasingly savvy and experienced about contracting with technology firms to address urban problems.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.246
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it