Book Reviews
Bibliographic record
Abstract
Carol Martin and Henry Bial, eds., Brecht Sourcbook, reviewed by John Fuegi Joseph Farrell and Antonio Scuderi, eds., Fo: Stage, Text, and Tradition, reviewed by Rosalind Kerr, David Williamd, eds., aborative Theatre: The Thetitre du Soleil Sourcebook, reviewed by Timothy Scheie Jean Graham-Jones, Exorcising History: Argentine Theater under Dictatorship, reviewed by Diana Taylor Stephen A. Marino, ed., "The Salesman Has a Birthday": Essays Celebrating the Fiftieth Anniversary of Arthur Miller's Death of a Salesman, reviewed by Diana Taylor Susan C.W. Abbotson, Student Companion to Arthur Miller, reviewed by Diana Taylor Brenda Murphy and Susan C.W. Abbotson, Understanding Death of a Salesman: A Student Casebook to Issues, reviewed by Jonathan Chambers Albert J. Devlin and Nancy M. Tischler, eds., The Selected Letters of Tennessee Williams. Vol I — 1920–1945, reviewed by Philip C. Kolin Philip C. Kolin, Williams: A Streetcar Named Desire, reviewed by George W. Crandell Christopher Bigsby, Contemporary American Playwrights, reviewed by Melanie Blood Gordon Rogoff, Vanishing Acts: Theater since the Sixties., reviewed by Stephen J. Bottoms Bonnie Marranca and Gautam Dashupta eds., Conversations on Art and Petformance, reviewed by Mark Fortier
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.509 | 0.525 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".