In Search of New Metaphors: An Interview with Linda Hutcheon
Bibliographic record
Abstract
Linda Hutcheon holds the rank of “University Professor” of English and Comparative at \nthe University of Toronto. She is the author and co-author of 10 books on topics that range \nfrom postmodernism to interdisciplinary approaches to opera, but her constant interest has \nbeen in critical theory and its intersections with contemporary culture, especially Canadian \nand American culture. Her most recent books include Splitting Images: Contemporary Canadian \nIronies (1991), Irony’s Edge: The Theory and Politics of Irony (1994) and, with Michael \nHutcheon, M.D., Opera: Desire, Disease, Death (1996) and Bodily Charm: Living Opera \n(2000). From 1994-2000, with Mario J. Valdes, she directed two large comparative literary \nhistory projects (on Latin America and on East Central Europe) to be published by Oxford \nUniversity Press. She currently sits on the board of 17 scholarly journals, and in 2000 was \nthe President of the Modern Language Association of America. \nThis interview was partially conducted on Monday, 17th July 2000 at 10:30 am. in Linda \nHutcheon’s office at the University of Toronto, and then completed through various email \nexchanges.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.021 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".