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Record W4416107999 · doi:10.1017/s0040557425100653

LeAnn Fields and University of Michigan Press

2025· article· en· W4416107999 on OpenAlexaff
Gina M. Di Salvo, Jill Dolan, Una Chaudhuri, Harry J. Elam, Marvin Carlson, Henry Bial, Carrie Sandahl, Harvey Young, Daniel Sack, Natalie Álvarez, Kareem Khubchandani, Christin Essin, E. H. Lee, Jill Dolan

Bibliographic record

VenueTheatre Survey · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsField (mathematics)Performance studiesTRACE (psycholinguistics)Work (physics)Chart

Abstract

fetched live from OpenAlex

In 1987, LeAnn Fields acquired Lynda Hart‘s Making a Spectacle: Feminist Essays on Contemporary Women’s Theatre. By the time Fields retired in 2024, she had built a list of more than 280 books in the field of theatre and performance studies at the University of Michigan Press. Hart’s Making a Spectacle is a foundational and still radical book of critical essays on gender, the body, and spectatorship, topics that continue to chart and reverberate among the many intellectual commitments of our field. Like nearly all the books that Fields acquired for University of Michigan Press, Making a Spectacle drew from and responded to another interdisciplinary field of study, women’s studies, as it simultaneously broke new ground in theatre and performance studies. In this special section, thirteen authors discuss the ways in which Fields encouraged the development of their work and our field. These author accounts are followed by an interview with Fields by Jill Dolan, in which Fields describes how her work as an acquisitions editor began and how it changed, how she navigated the press boards and changes in technology and staffing, and how, from her perspective, our field fosters a unique sense of community. The author accounts and interview offer an invaluable collection of personal histories that trace the development of our field over the past four decades to our vibrant present.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.027
GPT teacher head0.216
Teacher spread0.189 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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