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Record W3209025936 · doi:10.32920/ryerson.14647413.v1

Moody architecture: emotionally intelligent prostheses

2021· preprint· en· W3209025936 on OpenAlexaff
Julia Mozheyko

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsArchitectureCyberneticsPhenomenology (philosophy)DoctrineComputer scienceNormativeCognitive scienceCognitive architectureHuman–computer interactionCognitionPsychologyArtificial intelligenceEpistemologyLawNeuroscience

Abstract

fetched live from OpenAlex

The digital age has altered the prosthetic relationship between the body and its extensions. Communication devices have started to engage us in an emotional conversation, whereas the focus on the body in architecture perpetuates the mechanistic relationship that dominated during the industrial revolution. This lack of emotional connectivity in architecture challenges the idea of the normative body in light of an analysis of the relationship between empirical reality and classical doctrine. This thesis proposition envisages architecture as becoming an emotionally intelligent prosthesis endowed with anthropomorphic characteristics. Phenomenology and cybernetic systems provide the tools to advance the relationship between the body and its prosthetics. A feedback loop demonstrating cognition and plasticity is a prerequisite for structurally coupling two such systems. Architecture is conceived as evolving to interact continuously with the physical and emotional state of the user. This speculative world allows the thesis to consider how the body and the building might become the organs and prostheses of each other.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.935
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 integrity0.0000.001
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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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