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Record W2739779039 · doi:10.22230/cjc.2017v42n3a3178

De la cybernétique à l’architecture numérique : retour sur un demi-siècle de théories, pratiques et projets expérimentaux

2017· article· en· W2739779039 on OpenAlexvenueno aff
Marion Roussel

Bibliographic record

VenueCanadian Journal of Communication · 2017
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsCyberneticsArchitectureCyberspaceTheme (computing)InteractivitySociologyHumanitiesPosthumanismEpistemologyArt historyArtPhilosophyComputer scienceThe InternetVisual artsMultimediaWorld Wide Web

Abstract

fetched live from OpenAlex

Background Little is said about the influence of cybernetics on the architectural practices and theories of the past fifty years. Yet, from the discourses of the experimental architecture of the 1960s and 1970s, to those of the most advanced advocates of digital design, the terms and key themes of cybernetics keep coming back. Analysis Focusing on the first attempts to apply cybernetics to architecture, then to the themes of the network, cyborg and cyberspace, and finally to the biological turn of the architecture of the 2000s, this article shows the prevalence of notions of the interactivity, adaptability, reflexivity, sensitivity and intelligence of architecture and the city in architects’ discourses. Conclusion and Implications Architects have never stopped working on these cybernetics notions. Moreover, the latter are culminating today in the theme of the 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 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)0.029
metaresearch head score (Gemma)0.063
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0030.041
Scholarly communication0.0110.021
Open science0.0030.007
Research integrity0.0040.012
Insufficient payload (model declined to judge)0.0130.002

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 head0.008
GPT teacher head0.249
Teacher spread0.241 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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