Confronting Genre: Opera, Memorial, and John Greyson's <i>Fig Trees</i>
Bibliographic record
Abstract
Research Article| March 01 2010 Confronting Genre: Opera, Memorial, and John Greyson's Fig Trees Sarah Henstra Sarah Henstra Ryerson University shenstra@english.ryerson.ca Sarah Henstra is assistant professor of English at Ryerson University in Toronto, Canada. She is the author of The Counter-Memorial Impulse in Twentieth Century English Fiction (Palgrave Macmillan, 2009). Her current research focuses on mourning and remembrance in contemporary social activism campaigns. Search for other works by this author on: This Site Google English Language Notes (2010) 48 (1): 67–77. https://doi.org/10.1215/00138282-48.1.67 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Sarah Henstra; Confronting Genre: Opera, Memorial, and John Greyson's Fig Trees. English Language Notes 1 March 2010; 48 (1): 67–77. doi: https://doi.org/10.1215/00138282-48.1.67 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsEnglish Language Notes Search Advanced Search The text of this article is only available as a PDF. Copyright © 2010 Regents of the University of Colorado2010 Article PDF first page preview Close Modal Issue Section: II. Mourning, Melancholia, Trauma You do not currently have access to this content.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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".