I nheriting the W ind : A P ersonal V iew of the C urrent C risis in T heatre H igher E ducation in N ew Y ork
Bibliographic record
Abstract
Abstract: It is no secret, unhappily, that the study of theatre in the colleges and universities of this country is a discipline under siege, but the severity of the problems received strong confirmation in New York State this fall when two of the most distinguished and long-established (over a century in both cases) programs in the country were, with little warning, faced with draconian cuts or outright extinction. The fact that one, the state University of Albany, was the flagship school of the public system, and the other, Cornell University, was one of the state’s most distinguished private institutions, suggests the scope and impact of these actions. At Albany, four other programs are being terminated along with theatre—Classics, Russian, Spanish, and French—while at Cornell the extent of the severe cuts imposed on the theatre program—almost a quarter of the total budget of the department (which also shelters dance and film)—are being suffered by no other program in the university. The prominence of these two schools in a state that has long claimed a central position in American theatre makes them particularly significant symbolically of a discipline in crisis, and this has impelled me to engage in serious and sometimes painful reflections on that discipline, the basis of the present essay.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".