Deep Dramaturgy: Excavating the Architecture of the Site-Specific Performance
Bibliographic record
Abstract
Four-hundred-and-fifty people have gathered in a sculpture court. Small folding stools on the perimeter of the spacefill quickly. I am whispering requests to the artistic staff and catching the cast’s eyes to let them know we are ready. H and Shannon are tied together with rope, back-to-back on a six-foot high platform. They are both tall, and one faint moment could send them both crashing into the marble floor. I check that the safety ladder is close to them and give the go to Joe and Jeff on sound and lights. Images floating around me cohere and break apart, like clouds or nebulae. The sinking of the Titanic is buoyed up by a tap dancer in flapper garb, glowing at the audience as she spins in double time. We are wrenched back into the modem world with a pulsating dance that all thirty-seven actors whip into, an accumulating, repetitive movement that surges through the space, till the fragile wooden plinths look in danger of toppling. After the frenzy, a meditative text on the idea of home, and a grand finale of Vegas dancers, who break down like wind-up toys, littering the stage with their peacock feathers as they die in a parody of Swan Lake. The final text – echoing over the dancers’ bodies, the stilled tableaux – is doubly resonant for the company members who know it is their last performance together.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".