Bibliographic record
Abstract
Robots have been a source of both intrigue and anxiety for artists and a lively apparatus for study by scientific researchers for several decades. Though many people view robots as being cold, unemotional, and frightening, there is a growing field in robotics specifically focused on social applications including therapy, elder care, and the arts. Robots have been utilized extensively in installation art works and sculpture, but the performing arts have been somewhat more resistant to them. Machines which have all the technical abilities to perform tasks, such as playing an instrument or executing choreography without fatiguing or making errors, can be threatening to human performers who have honed these abilities and rely upon them for creative expression and their livelihoods. By synthesizing studies in the scientific field of social robotics, philosophical insight into technology and the arts, and case studies of robots used in dance and other art forms, I seek to provide an alternative point of view of robotic integration into performance. Robots do not need to act only as avatars of human beings, they can be effectively utilized in dance to expand upon the capabilities of the human body, act as automatic ‘puppets’ for choreography, integrate into human performance, and be ‘autonomous’ performers in their own right. Robot dancers do not inherently replace or devalue human artists; instead, they can provide complex insight into the understanding of human bodies, emotions, and technology.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.053 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".