Reimagining Living Ontologies: An immersive cross-disciplinary collaborative performance that combines biophysical data, generative patterns and improvisation
Bibliographic record
Abstract
<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d8937203e64">Reimagining Living Ontologies is an innovative and improvisational collaborative data art performance with responsive visuals incorporating scientific and artistic approaches in data visualisation and interpretation systems. This project utilises biophysical data from the human heart, arm and wrist muscles that drive computer-generated audio-visual scenery inside an immersive 360-degree video projection dome located at York University (Toronto, Canada) in Cinema & Media Arts research location <i>BetaSpace</i>. The core research questions and objectives that drive this project are: 1) evaluate current data practices within artistic and scientific realms; 2) identify the practices and challenges that are concerned with biophysical data harnessing and interpretation; 3) develop an artistically rich and innovative data artwork that builds on mutually agreeable data transactions and innovative technologies; 4) propose, exhibit and perform creative knowledge building systems that are embedded in artistic and research-creation domains.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".