Bibliographic record
Abstract
As historians often note, the success of the comic book industry in North America was indelibly linked to the rise of superheroes; yet it was virtually impossible for Canadian comic books to compete with US imports, so early superheroes were almost exclusively American. This situation changed briefly during WWII, when a ban on imports allowed Canadian artists to develop their own national superheroes, like Johnny Canuck; yet the ban was lifted soon after the war, and the nation was once again flooded with American comics. It wasn’t until the 1970s and 1980s that Canadian publishers began to introduce new Canadian superheroes, like Captain Canuck and Northguard, in a period that became known as the “Silver Age” of Canadian comics. The enduring popularity of these characters shows that there is still a desire on the part of many readers to consume superhero narratives featuring Canadian characters, and these narratives often frame Canadian identity in opposition or relation to American identity due to the economic dominance of American publishers and the close connection between the superhero genre and American popular culture. This chapter examines the history of Canadian superheroes, and it particularly focuses on how this history reflects an ongoing struggle to imagine an appropriate symbol of Canadian identity, which has proven to be extremely difficult within a marketplace dominated by American companies and characters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".