Bibliographic record
Abstract
The Dance Machine is a durational and participatory installation of rope, pulleys, bamboo, and cedar. Au-dience members’ engagement with the machine is facilitated by dance artists, who prepare for this task by learning from Indigenous knowledge holders and assembling the machine alongside technicians. Piecing together archival fragments in dialogue with Lee Su-Feh, I discuss how the Dance Machine initially was developed in extension of Lee’s body and eventually became her ‘outside body’ – inviting consensual envi-ronmental and interpersonal interactions. In pursuit of such connections, Lee’s journey with the Dance Machine moved through: (1) learning and teaching how to listen to the machine, and (2) querying what she does not know about herself as a colonized Malaysian body and about the displaced Indigenous peoples whose land she occupies in Canada. A potential for relational change seems to emerge from the self-organizing dramaturgy of the Dance Machine. To understand this potential, I first (tentatively) identify the Dance Machine’s generating components and the patterns of engagement they attract. Then I theorize the material performativity and capacity for change of these dynamics while relating them to Lee’s parallel journey.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".