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Record W4413057374 · doi:10.1177/0160323x251343025

Do Small Towns Have Big Smart City Dreams?

2025· article· en· W4413057374 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

VenueState and Local Government Review · 2025
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsDe factoIndigenousEquity (law)Big dataSmart citySmart growthPolitical sciencePublic relationsBusinessRegional sciencePublic administrationEconomic growthSociologyUrban planningInternet of ThingsEngineeringEconomicsComputer scienceInternet privacy

Abstract

fetched live from OpenAlex

Although many angles of the smart cities’ movement have been well-studied by academia, a gap remains in our understanding of how municipal policymakers understand and apply the concept in their localized contexts—particularly how community size affects smart city ambitions. In 2017, Infrastructure Canada announced its Smart City Challenge (SCC), asked communities across Canada—municipalities, local or regional governments, and Indigenous communities—to design creative and innovative solutions to address any societal problem using any data and connected technology solution. The application process was a de facto survey that generated a unique, publicly available dataset from the 199 applicant communities, big and small alike. We find that larger communities submit more ambitious proposals to use latest technologies to address social equity and inclusion concerns through expanded operations, whereas smaller, more rural municipalities focus more on basic infrastructure and services. Though there is overlap, their dreams differ.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score0.495

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 head0.016
GPT teacher head0.221
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