Bibliographic record
Abstract
I look at an image of my performance of Free Hugs, an interactive dance installation that sought to activate Tkaronto’s Lisgar Park amid the city’s attempts to reopen during the COVID-19 pandemic in the summer of 2020. More than a year later, a lot has changed with the development of vaccines and the lifting of restrictions/regulations for in-person gatherings. And yet I am still uncertain of how to connect with others amid growing concerns about the pandemic’s fourth wave. Hoping that I will rediscover some insight from my performance to relieve me of my uncertainty, I return to this image. It captures a moment of my attempt to share a hug with my audience—to connect with them in an intimate way amid the borders of physical distance imposed on us and by us. Looking at this visual representation of my performance, however, I feel my distance from the experience of my dancing body and all that it tries to teach me. I desire an embodied connection with the gestures of that performance to guide me forward into our uncertain future. Turning to disability arts practices inspired by questions and aesthetics of access, I release a practice through which we might simultaneously rest within and wrestle through the creation, navigation, and transcendence of distance to access connection.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".