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Record W3106618691 · doi:10.22215/etd/2018-12841

Drawing on Context: Proposing a Hybrid Design Process for Textured Concrete

2018· dissertation· en· W3106618691 on OpenAlexaff
Johanna E. Abril

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsCarleton University
Fundersnot available
KeywordsRepresentation (politics)Process (computing)Context (archaeology)Computer scienceArchitectureArchitectural engineeringArchitectural designOrder (exchange)EngineeringEngineering drawingVisual artsArtGeographyProgramming language

Abstract

fetched live from OpenAlex

This thesis argues that making architectural representations using combinations of different mediums and tools offers greater material awareness, which opens the possibility of closer engagement with the site and its program. Specifically, the first chapter compares the strengths of digital and non-digital drawing mediums in order to develop an architectural theory that promotes the development of hybrid representations. The second chapter is a case study relating the method of drawing and translation to construction of two projects that use textured concrete. The third chapter details how a hybrid representation is used to design an addition to a culinary academy in the city of Cuenca, Ecuador. Finally, the research concludes that the process of making a hybrid representation takes advantage of the strengths of digital and non-digital mediums creating a closer relationship between materials and site.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.264
Teacher spread0.247 · 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 designSimulation or modeling
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
Published2018
Admission routes1
Has abstractyes

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