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Record W2014571534 · doi:10.1002/ad.1712

Why We Need Architecture of Tolerance

2014· article· en· W2014571534 on OpenAlexaboutno aff
Branko Kolarevic

Bibliographic record

VenueArchitectural Design · 2014
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectureZero toleranceReputationHoly GrailNegotiationArchitectural engineeringEngineering managementComputer scienceSociologyEngineering ethicsEngineeringLawPolitical scienceWorld Wide WebVisual artsArt

Abstract

fetched live from OpenAlex

Abstract Professor and Chair in Integrated Design at the University of Calgary Faculty of Environmental Design, Branko Kolarevic has established an international reputation as an author and commentator on digital design and manufacturing in architecture. Here he questions the importance of high‐definition tolerance in construction itself. As an industry that operates at far greater sizes and scales than other manufacturing bases, has the ‘messy world of building’ not always had to allow for a reasonable tolerance of errors? Should high‐definition processes, which negate or negotiate zero tolerance, really be for architects and construction professionals the Holy Grail that they first appear?

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)0.008
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.040
Scholarly communication0.0110.023
Open science0.0020.007
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0130.003

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 head0.010
GPT teacher head0.190
Teacher spread0.181 · 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 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

Citations10
Published2014
Admission routes1
Has abstractyes

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