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Record W2169950322 · doi:10.1017/s0040557411000093

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

2011· article· en· W2169950322 on OpenAlexaboutno aff
Marvin Carlson

Bibliographic record

VenueTheatre Survey · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSiegeState (computer science)Quarter (Canadian coin)DancePolitical scienceScope (computer science)HistoryEconomic historyMedia studiesArtSociologyVisual artsAncient history

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.100
GPT teacher head0.243
Teacher spread0.143 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2011
Admission routes1
Has abstractyes

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