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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.266 | 0.108 |
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".