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Record W4280558501 · doi:10.32920/19750354.v1

Making public spaces smarter

2022· preprint· en· W4280558501 on OpenAlexafffundabout
Danielle Lenarcic Biss

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsToronto Metropolitan UniversityMount Allison University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRealmInteractive kioskBusinessPlan (archaeology)Public relationsCorporatizationSmart cityEnablingPolitical scienceInternet privacyInternet of ThingsComputer scienceWorld Wide WebGeography

Abstract

fetched live from OpenAlex

<p>Technologies are redefining public gathering spaces and changing our collective experiences in the city. Digital and smart tools bring opportunities like improved efficiency, responsiveness, and civic participation but also concerns of privacy, data management, and corporatization. This research explored the extent to which 137 Canadian communities are planning for use of technology in the public realm. Content analyses of planning policies and Smart Cities Challenge applications indicated that one third of communities proposed using technologies in public spaces. Findings suggest that Canadian municipalities are aspirational in their intentions and in the early stages of anticipating how to plan for and implement these tools. There is interest in experimenting with hubs for charging mobile devices, kiosks/screens for sharing information, smart transportation infrastructure, and smart street furniture. Recommendations for planners, planning schools, partnerships, design professionals, and Smart Cities Challenge stakeholders are highlighted.</p> <p>Key words: Smart Cities Challenge, public realm, civic technology, innovation, planning policy</p>

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 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 categoriesInsufficient payload (model declined to judge)
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 score0.737
Threshold uncertainty score0.997

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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.052
GPT teacher head0.249
Teacher spread0.197 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes3
Has abstractyes

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