The Canadian Alternative: Cartoonists, Comics, and Graphic Novels
Bibliographic record
Abstract
"Contributions by Jordan Bolay, Ian Brodie, Jocelyn Sakal Froese, Dominick Grace, Eric Hoffman, Paddy Johnston, Ivan Kocmarek, Jessica Langston, Judith Leggatt, Daniel Marrone, Mark J. McLaughlin, Joan Ormrod, Laura A. Pearson, Annick Pellegrin, Mihaela Precup, Jason Sacks, and Ruth-Ellen St. Onge. This overview of the history of Canadian comics explores acclaimed as well as unfamiliar artists. Contributors look at the myriad ways that English-language, Francophone, indigenous, and queer Canadian comics and cartoonists pose alternatives to American comics, to dominant perceptions, even to gender and racial categories. In contrast to the United States' melting pot, Canada has been understood to comprise a social, cultural, and ethnic mosaic, with distinct cultural variation as part of its identity. This volume reveals differences that often reflect in highly regional and localized comics such as Paul MacKinnon's Cape Breton...specific Old Trout Funnies, Michel Rabagliati's Montreal-based Paul comics, and Kurt Martell and Christopher Merkley's Thunder Bay...specific zombie apocalypse. The collection also considers some of the conventionally "alternative" cartoonists, namely Seth, Dave Sim, and Chester Brown. It offers alternate views of the diverse and engaging work of two very different Canadian cartoonists who bring their own alternatives into play: Jeff Lemire in his bridging of Canadian/US and mainstream/alternative sensibilities and Nina Bunjevac in her own blending of realism and fantasy as well as of insider/outsider status. Despite an upsurge in research on Canadian comics, there is still remarkably little written about most major and all minor Canadian cartoonists. This volume provides insight into some of the lesser-known Canadian alternatives still awaiting full exploration."...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".