Bibliographic record
Abstract
As an artist, thinking about live digital performance, I have always been curious about the intersection between media and wellness and how technology could possibly be applied in a therapeutic setting. I investigated these intersections at FOLDA through participant interaction with live motion capture rendering, at Queens Ingenuity labs, and through social inquiry of virtual space with Prof. M Wheeler at the Isabel Bader Center. This research continued with VR experimentation at the Eastern bloc residency in Montreal hosted by UQAM | Université du Québec à Montréal and the Connected Minds CFREF launch. Finally I was given the opportunity to further my findings through a project creation residency with the Kick and Push Festival. After inquiring into live digital performance, and its broader connections to wellness, I have found that technology can bring audiences together when they are tasked with using it together. The more social and accessible technology is, the more effective it is with producing performance that audiences will also enjoy. This research makes me wonder if audiences prefer to view and interact with performance with technology but not through it.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".