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Record W4384697343 · doi:10.22215/etd/2023-15617

Still Motion: Drawing Movement toward Adaptability in Time

2023· dissertation· en· W4384697343 on OpenAlexaff
Dana Maria Mastrangelo

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsCarleton University
Fundersnot available
KeywordsArchitectureMovement (music)AdaptabilityMotion (physics)Space (punctuation)Representation (politics)Architectural engineeringComputer scienceContext (archaeology)SalientHuman–computer interactionEngineeringVisual artsAestheticsArtificial intelligenceArtHistory

Abstract

fetched live from OpenAlex

Within the built environment, buildings often behave as inanimate objects, unable to act and react to their occupants or surroundings. These characterless elements are found in structures that idle as unresponsive, unable to evolve and adapt in time. Since architecture can be described as the thoughtful art of making space, it would seem trivial for architects to render buildings independent from ‘time’ and ‘motion,’ as the space-time continuum is inherently linked to movement. However, architectural drawings regularly illustrate static depictions of moments frozen in time, reducing the dimensional performance of space to a flattened, silent plane. Designed as a guide for depicting movement in architectural drawings, this thesis aims to invite architects to consider a building’s active rapport with its users and context. I seek to study and discuss the interplay between architecture and its representation, investigating the outcomes of adaptability when derived from traces of movement in architectural drawings.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.772

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)0.0000.001

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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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 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
Published2023
Admission routes1
Has abstractyes

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