S even W ays of L ooking at the A merican S ociety for T heatre R esearch
Bibliographic record
Abstract
Abstract: It was my first ASTR conference, a gathering on “Popular Entertainment” held over two decades ago in New York City, and every time my tweed-clad, bearded husband and I neared the conference arena, he was mistaken for the ASTR member. Determined to prove myself a scholar through my performance if not my looks, I strode into the dimmed conference auditorium, sat near the front, and listened intently to the chair of the next panel as he announced that, “unfortunately,” the aging vaudeville star scheduled to perform the fan dance for us had fallen ill. While I tried to process that information, a leotard-covered American Ballet Theatre replacement glided onto the stage, fan in hand. I was transfixed. I knew I was inexperienced—I had only attended two other theatre conferences—but was this really happening? At my first theatre conference prior to ASTR’s—an inexplicably weeklong sojourn dedicated to Sarah Bernhardt—I had watched the aging French diva stomp around in an early silent movie as an independent collector slavered over “Sar-aah’s divine ah-rt” and Laurence Senelick offered me a simultaneous (and inimitable) sotto voce commentary. At my second conference, I had found myself engaged in a group sing-along of a nineteenth-century barroom ditty and had encountered my still-favorite opening line: “Canadian provincial theatre is an almost virgin field.”
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".