Bibliographic record
Abstract
Abstract: This essay draws on a series of recent in-studio conversations and collaborations with different Vancouver dance artists (James Gnam and Natalie Lefebvre Gnam of plastic orchid factory, Ziyian Kwan of dumb instrument Dance, and Tara Cheyenne Friedenberg of Tara Cheyenne Performance) to ask how we value and make meaningful the process that leads to the performance product. Following from Shannon Jackson, I seek to make visible—to show—the various structures of support, from the operational to the affective, that underpin and enable this process. How might both the precarity (financial and physical) and the motility (locomotive and emotive) specific to dance as discipline help us to rethink the different economies of scale that performance engages: from the abstractly derivative and all-encompassing markets of global commerce to the resolutely embodied and intimate spaces of local collaboration? In attempting to answer this question, I am also seeking to account for the work of living—for both my dance-artist friends and myself—that goes into making and supporting a work of live art.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".