Bibliographic record
Abstract
Compares the cultural productions of Canada and the US - literature, but also film, opera, and even theme parks - providing a reassessment of Canadian Studies within a comparative framework. Since the elections of Donald Trump and Justin Trudeau, unprecedented international attention is being drawn to the differences between the United States and Canada. This timely volume takes a close comparative look at the national imaginaries of the two countries. In its analyses of the two countries' cultural productions - literature, but also film, opera, and even theme parks - it follows the approach of Comparative North American Studies, which has been significantly advanced by Reingard M. Nischik's work over recent decades. Featuring such illustrious contributors as Linda Hutcheon, Sherrill Grace, and Aritha van Herk, the volume considers the works of writers such as MargaretAtwood, whose concern with both countries' identities is well known, but also offers surprising new insights, for example by comparing writing by Edgar Allan Poe with Canadian Yann Martel's novel Life of Pi and Nobel Prize-winning author Alice Munro's work with that of the American graphic novelist Alison Bechdel. Contributors: Margaret Atwood, Shuli Barzilai, Julia Breitbach, Jutta Ernst, Florian Freitag, Marlene Goldman, Sherrill Grace, Michael and Linda Hutcheon, Bettina Mack, Silvia Mergenthal, Claire Omhovère, Katja Sarkowsky, Aritha van Herk. Eva Gruber is Assistant Professor of American Literature at the University of Konstanz. Caroline Rosenthal is Professor of American Literature at the University of Jena.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.633 | 0.323 |
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".