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Record W7043851572

Towards Conviviality in Smart Territories

2022· book-chapter· en· W7043851572 on OpenAlexaboutno aff

Bibliographic record

VenueArchivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · 2022
Typebook-chapter
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAbandonment (legal)Corporate governanceOrder (exchange)DelegationSociotechnical systemPoliticsSmart cityNarrative
DOInot available

Abstract

fetched live from OpenAlex

Today, smart planning models are beginning to show aspects of fragility, with Sidewalk Labs’ abandonment of the Quayside Toronto project being an emblematic example. This fragility indicates the need to deconstruct these powerful narratives and reflect on the appropriate application of ICT devices to spatial planning. The article aims to analyse the power of the dominant narrative, that of the smart city, which confers to the models of territorial development a conceptual value finalised in the production of economic value. The smart models present a series of limitations: Spatial ones, by creating a disconnection between physical and digital spaces; Political ones, by reducing territorial governance to its technical dimension; Social ones, by externalising and privatising public services. These limitations imply the delegation of the control of these technological devices with which we interact. After the analysis of smart planning, the article will explore an alternative approach regarding the use of technology. This approach will be based on Ivan Illich’s conviviality theory, and will aim to examine the development of smart territories as a place of reintegration of social relations and community in order to outline a different relationship with technology. Could technology establish an ethical relationship between territory and community?

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.201
Teacher spread0.185 · 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
GenreOther

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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