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Record W4402034697 · doi:10.32920/26883655.v1

Ripple: A Wearable Environment : Exploring Subspace Through an Experimental, Large Scale Textile Installation

2024· preprint· en· W4402034697 on OpenAlexaff
Deanna Armenti

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsSheridan CollegeToronto Metropolitan University
Fundersnot available
KeywordsTextileWearable computerScale (ratio)Subspace topologyRippleWearable technologyComputer scienceEngineeringArtificial intelligenceGeographyCartographyEmbedded systemElectrical engineeringArchaeology

Abstract

fetched live from OpenAlex

<p><em>Ripple: A Wearable Environment</em> is an experimental large-scale textile installation that explores subspace. Within the kink community, subspace has been explained as a meditative, dream-like state that feels like floating in water which is experienced when engaging in BDSM scenes. However, since most submissives experience subspace in a multitude of ways, the liminal temporality of subspace has remained a vague and generalized phenomenon. Experimental practice-based research techniques are utilized to dive deeper into subspace by exploring the flow of subspace and fetish items as talismanic sacred objects through an embodied lens. </p> <p><em>Ripple</em> pushes against the misconceptions of the queer, kink experience through redefining not only fetish fashion, but also what sexy is and can be. Throughout the project the use of unconventional colour palettes, loose knit and free form crochet creates an "anti-aesthetic" to the well-known styles found in fetish fashion. The intention is not to make queer kink and fetish fashion more palatable to the masses, but rather to create an embodied wearable that speaks more genuinely to the emotional and internal experience of submissive kinksters.</p>

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
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)0.0010.000

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 head0.034
GPT teacher head0.247
Teacher spread0.213 · 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
Published2024
Admission routes1
Has abstractyes

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