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Record W7072157746

Writings on architecture and the city

2015· article· en· W7072157746 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectureHistory of architectureGeorge (robot)UrbanismUrban designArchitectural designUrban planningPrincipal (computer security)
DOInot available

Abstract

fetched live from OpenAlex

Writings on Architecture' is an anthology of texts by George Baird, focusing on his on-going interest in planning and the built environment, something which is particularly manifest in his attention to the city of Toronto, where he is active in architecture, urban design and heritage preservation.0After graduating from the University of Toronto in 1962, and then from University College, London, England, Baird went on to teach architectural theory and design at the Royal College of Art, and the Architectural Association School of Architecture in London, returning to Toronto in 1967. There, he founded his architectural practice, and joined the faculty of architecture at the University of Toronto and the faculty of the Graduate School of Design at Harvard University, where he was the G Ware Travelstead Professor of Architecture, and Director of the M Arch I and M Arch II Programs. From 2005 to 2009 Baird was Dean of the Faculty of Architecture, Landscape, and Design at the University of Toronto.0A principal author of the pioneering 1974 urban design study 'Onbuildingdowntown', he is the author/editor of numerous books, including 'Meaning in Architecture' (with Charles Jencks), 1968; 'Alvar Aalto', 1969; 'The Space of Appearance', 1995; and 'Queues, Rendezvous, Riots' (with Mark Lewis), 1995.The book includes an introductory essay by Louis Martin and is essential reading for those interested in architecture, architectural history and theory, urbanism and 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 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 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 score0.528
Threshold uncertainty score0.204

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 science0.0000.000
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 head0.217
Teacher spread0.193 · 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.

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
Published2015
Admission routes1
Has abstractyes

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