Review Of "Tandem Dances: Choreographing Immersive Performance" By J. Ritter
Bibliographic record
Abstract
This is Ritter's first book, and in it she employs dance and choreography as key structural elements in discussing immersive theater performances. Ritter (Rutgers Univ.) suggests that immersive events are distinctive because they involve both performers and audiences in enacting tandem movement scores. She begins with a theoretical orientation and then explores several tandem contexts in which choreography is central, including virtual reality and video gaming. Discussed first are the collaborative interactions and authorships shared between choreographers and dancers evident in set sequences and improvisation. The book then investigates ways that these dialogues have been manifest in the choreographic interactions among performances, performers, and audiences over time in various dance and theater productions. Ritter examines in detail three specific, long-running performances by companies in Canada, the UK, and the US, using these performances to explicate her theoretical perspective and illustrate the central significance of choreography to immersive performance overall in a 21st-century Western framework. Extensive endnotes and numerous black-and-white photos and illustrations provide useful support to the text. Summing Up: Recommended. Upper-division undergraduates through faculty; professionals.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".