LeAnn Fields and University of Michigan Press
Bibliographic record
Abstract
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.009 |
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".