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Record W4200232233 · doi:10.32920/16811473.v1

Corporeality: a haptic space

2021· preprint· en· W4200232233 on OpenAlexaff
Nona Arezehgar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsToronto Metropolitan UniversitySociety for the Study of Architecture in Canada
Fundersnot available
KeywordsHaptic technologyPerceptionSpace (punctuation)Haptic perceptionSightArchitecturePsychologyCognitive scienceCommunicationComputer scienceAestheticsCognitive psychologyHuman–computer interactionArtificial intelligenceVisual artsArtNeuroscience

Abstract

fetched live from OpenAlex

<div>The hegemony of vision and the suppression of other sensory realms has led to an architecture distanced from the human body. Undoubtedly, vision has the ability to receive the greatest amount of information from our surroundings; hence, it has been considered as primary to our perception. However, its interconnection with other bodily sensations is essential to perceive the totality of space; this connection also compensates for the limitations of sight. The purpose of this critique is not to demonize visuality; it is to consider the rhizomatic and interconnected nature of haptic perception of space. Approaching corporeality results in haptic spaces that enhance or suppress our bodily experience of spatial qualities while sharpening our visual experience. A haptic space will introduce more possibilities for bodily actions by focusing on spatiality, unifying the architecture of the foreground with the background. The concept of spatiality merges space and movement of the body, and therefore it can support or suppress the actions. These actions are subjectively performed based on perceived spatial opportunities through haptic perception. The thesis is intended to explore possibilities embedded within haptic space to create a richer architectural experience. It will explore the spatial interconnections between haptic perception, somatosensory system, vision and consequently bodily movements.</div>

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: none
Teacher disagreement score0.963
Threshold uncertainty score0.565

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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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
Published2021
Admission routes1
Has abstractyes

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