Bibliographic record
Abstract
INCANTATIONS is a collection of sixteen visual and sonic experiments centred around the idea of score as spell. The project was curated and produced for The Witch Institute in 2021 by Brandon Hocura and Naomi Okabe, who together run the record label Séance Centre. Pairing a visual/test-based artist with a musician/composer, they commissioned original works in which a spell (visual score) was interpreted and manifested in sound. The contributing artists include Felicia Atkinson (Belgium), bill bissett (Canada), adrienne maree brown (USA), Scott Gailey (Canada), C.R. Gillespie (Canada), Beverly Glenn-Copeland (Canada), David Horvitz (USA), Benjamin Kilchofer (Switzerland), Idris Rahman (UK), Mehrnaz Rohbakhsh (Canada), Gavilan Rayna Russom (USA), Tomoko Sauvage (Japan/France), Dani Spinosa (Canada), Mona Steinwidder/Museum Of No Art (Germany), Yu Su (China/Canada), Andrew Zukerman (Canada).
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.204 | 0.072 |
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".