Bibliographic record
Abstract
Scored in Silence is a digital artwork and performance by Chisato Minamimura that unpacks the untold tales of deaf hibakusha – survivors of the A-Bombs that fell in Hiroshima and Nagasaki in 1945 - and their experiences at the time and thereafter. It explores technologically mediated performance through a range of practices including holo-projection and vibrotactility. Developed in collaboration with a team of digital artists and supported by Vibrofusion Lab, Scored in Silence features the work of Jon Armstrong (lighting/projection design) Danny Bright (sound/vibration composition) and Dave Packer (animation). Scored in Silence is produced by Sarah Pickthall. Scored in Silence employs Woojer© vibration straps worn on the body for audience members to feel the haunting soundscape and vibrational composition. The work also employs Holo-Gauze © projection that meshes Chisato’s sign mime performance with stunning animation. The work exists in three formats: as a live theatrical performance, full-length performance film, and gallery installation. The work has been supported by Arts Council England, British Council, Canada Council for the Arts, the Great Britain Saskawa Foundation, with in-kind support from Woojer©. Performances: 14th & 15th September 2018 - ONCA, Brighton, UK (Brighton Digital Festival) 19th & 20th October 2018 - The International Anthony Burgess Foundation, Manchester, UK (Manchester Science Festival) 18th May 2019 - Fanshawe College, Hamilton ON, Canada 20th & 21st July 2019 - Ovalhouse, London, UK 19th to 23rd August 2019 - Greenside Emerald Theatre, Edinburgh, UK (British Council Showcase at Edinburgh Fringe Festival) 11th December 2019 - Théâtre Jeunes Créatuers, Tunis, Tunisia 13th December 2019 - Municipal Theatre, Sfax, Tunisia
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.459 | 0.251 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".