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Record W2797878944 · doi:10.31235/osf.io/e4fs8

Open Smart Cities in Canada: Environmental Scan and Case Studies - Executive Summary

2018· preprint· en· W2797878944 on OpenAlexaboutno aff
Tracey P. Lauriault, Rachel Bloom, Carly Livingstone, Jean-Noé Landry

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSmart cityOpen governmentGovernment (linguistics)BusinessExecutive summaryExecutive directorOpen dataPublic relationsEngineeringPolitical scienceManagementFinanceEconomicsInternet of Things

Abstract

fetched live from OpenAlex

This executive summary consolidates findings from a smart city environmental scan (E-Scan) and five case studies of smart city initiatives in Canada. The E-Scan entailed compiling and reviewing documents and definitions produced by smart city vendors, think tanks, associations, consulting firms, standards organizations, conferences, civil society organizations, including critical academic literature, government reports, marketing material, specifications and requirements documents. This research was motivated by a desire to identify international shapers of smart cities and to better understand what differentiates a smart city from an Open Smart City.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score1.000

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 science0.0000.003
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 head0.024
GPT teacher head0.227
Teacher spread0.203 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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