Ripple: A Wearable Environment : Exploring Subspace Through an Experimental, Large Scale Textile Installation
Bibliographic record
Abstract
Ripple: A Wearable Environment 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. Ripple 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.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".