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Record W4291161965 · doi:10.3389/frsc.2022.960400

Why government supported smart city initiatives fail: Examining community risk and benefit agreements as a missing link to accountability for equity-seeking groups

2022· article· en· W4291161965 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.

Bibliographic record

VenueFrontiers in Sustainable Cities · 2022
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsEquity (law)AccountabilityGovernment (linguistics)Smart cityAdaptation (eye)BusinessPublic relationsRelevance (law)Public economicsPolitical scienceEconomicsPsychologyInternet of ThingsComputer securityComputer science

Abstract

fetched live from OpenAlex

This paper utilizes concepts from a critical social justice discourse on smart cities to identify factors behind resistance to new smart city initiatives from equity-seeking groups. The broader critical discourse is examined based on relevance to the eventual failure of the initiatives selected as case studies. It highlights institutional failure within government-supported initiatives due to the lack of consideration given to equitable distribution of risks and formal accountability mechanisms. It describes outcomes surrounding smart cities in which the benefits accrue to some groups within the city while risks increase for other groups. Finally, we examine the integration of “risk” as an adaptation to the existing practical mechanism of Community Benefit Agreements, for use of this framework to support value sensitive design approaches in future smart city initiatives.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.028
GPT teacher head0.260
Teacher spread0.232 · 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