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It’s a Smart World? An Architectural Reflection on Smart Cities through Hannah Arendt’s Notion of the World

2022· article· en· W4285308870 on OpenAlexaboutno aff
Hans Teerds

Bibliographic record

VenueArendt studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Studies and Postmodernism
Canadian institutionsnot available
Fundersnot available
KeywordsReflection (computer programming)AestheticsArchitectural engineeringArtSociologyArt historyEngineeringComputer science

Abstract

fetched live from OpenAlex

This paper challenges the ideas beyond the application of smart technology in the urban environment by investigating the proposal for the waterfront of Toronto by Sidewalk Labs. Although the project has been cancelled in the first months of the COVID pandemic outbreak, it still offers a valuable case study, as it was developed by Sidewalk Labs, part of Alphabet Inc, the company behind, among others, Google. This paper focusses on the spatial, material, and political aspects of the proposal, which are investigated through an architectural reading of Hannah Arendt’s notion of the world. The paper reflects on the public spaces in the plan, and in particular to the ambition to make these spaces “responsive” to popular demand. This ideal is inherent to the most far-fledged convictions beyond smart cities. In contradiction to its promising images and wild ideas, this paper concludes that it silences the participants and diminishes the possibility of active participation in the built environment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.071
Scholarly communication0.0080.010
Open science0.0010.007
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.163
GPT teacher head0.387
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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 routes1
Has abstractyes

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