Bibliographic record
Abstract
Wendy Steiner is the Richard L. Fisher Professor of English Emerita at the University of Pennsylvania. Professor Steiner earned her B.A. from McGill University in 1970 and both her M.Phil. and Ph.D. in English from Yale University in 1972 and 1974 respectively. After teaching at Yale (1974-1976) and the University of Michigan (1976-1979), she joined the Penn faculty in 1979. Promoted to associate professor three years later, she was named full professor in 1985. At Penn, she served as Chair of the English Department from 1995-1999, Founding Director of the Penn Humanities Forum from 1998-2010, Master of Modern Languages College House from 1985-1988, and director of the Penn/King’s College Program in London from 1989-1990. Professor Steiner’s fields are interartistic relations and literature in English of the 20th and 21st centuries. Among her books on modern literature and visual art are The Real Real Thing: The Model in the Mirror of Art (2010); Venus in Exile: The Rejection of Beauty in Twentieth-Century Art (2001). Professor Steiner has received awards from the Guggenheim, ACLS, and Mellon Foundations among others, and her cultural reviews have appeared widely in U.S. and British periodicals, including The New York Times, London Review of Books, Los Angeles Times, and The Guardian. Javad Khorsandi, Ph.D. student of English Language and Literature at Shiraz University has arranged this interview with Professor Steiner.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".