Comics of the New Europe : Reflections and Intersections
Bibliographic record
Abstract
A new generation of European cartoonists Bringing together the work of an array of North American and European scholars, this collection highlights a previously unexamined area within global comics studies. It analyses comics from countries formerly behind the Iron Curtain like East Germany, Poland, Czech Republic, Hungary, Romania, Yugoslavia, and Ukraine, given their shared history of WWII and communism. In addition to situating these graphic narratives in their national and subnational contexts, Comics of the New Europe pays particular attention to transnational connections along the common themes of nostalgia, memoir, and life under communism. The essays offer insights into a new generation of European cartoonists that looks forward, inspired and informed by traditions from Franco-Belgian and American comics, and back, as they use the medium of comics to reexamine and reevaluate not only their national pasts and respective comics traditions but also their own post-1989 identities and experiences. Contributors: Max Bledstein (University of Winnipeg), Dragana Obradović (University of Toronto), Aleksandra Sekulić (University of Arts in Belgrade), Pavel Kořínek (Institute of Czech Literature, Czech Academy of Sciences in Prague), Martin Foret (Palacký University), Michael Scholz (Uppsala University), Sean Eedy (Carleton University), Elizabeth Nijdam (University of British Columbia), Ewa Stańczyk (University of Amsterdam), Eszter Szép (Eötvös Loránd University) This publication is GPRC-labeled (Guaranteed Peer-Reviewed Content). In this video Martha Kuhlman discusses various aspects of the book 'Comics of the New Europe', focusing in particular on Czech authors.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".