Circuits of Sand and Water (Dramaturg)
Bibliographic record
Abstract
Circuits of Sand and Water is an immersive headphone theatre installation/performance by Prachi Khandekar. I have been working as Dramaturg on the project to develop the text, technology and audience engagement. This submission represents a public prototype of the performance testing the relationship between headphones, objects and environments at Wimbledon College of Art. The project explores the wager of privacy in pursuit of data-driven utopias. It examines the foundations of AI, a technology that's built on information mined from bodies and beings. Spectators enter an apartment and piece together the story of its resident: a woman who watches neighbours from her window during the pandemic. Her desire to analyse the neighbours is complicated by her innate empathy. She finds herself unable to function like an algorithm, and questions whether this makes her superior or inferior to the technologies built to serve us. The installation resembles an open-world game, in which you can collect snippets of the protagonist’s monologue by interacting with objects in her home. As you navigate deeper, you will hear what it’s like to be a human stuck between animal instincts and digital impulses. Developed with support from Canada Council for the Arts and CALQ.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.178 | 0.023 |
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".