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JALDA's Interview with Professor Wendy Steiner

2021· article· en· W3208442432 on OpenAlexaboutno aff
Wendy Steiner, Javad Khorsandi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsArt historyOperaArtSociologyClassics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0160.003
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0170.008

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.

Opus teacher head0.103
GPT teacher head0.415
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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