Serialization, Solipsism, and Swarming: American Politics and the Graphic Novel in the 1970s
Bibliographic record
Abstract
Abstract Richard Howell’s Glamazon’s Burden, a graphic novel initially serialized between 1977 and 1979, makes a wry allusion to midcentury literary critic Leslie Fiedler in the shape of a book that never existed: Love and Death in the Comic Novel. Prompted by this subtle aside, which implies that the comic or graphic novel might be the vessel for anxious reflection on the state of American democracy, this article explores what the development of long-form comics in the 1970s meant for the articulation of political possibilities. This was the decade when American public commentators—not least President Carter—lamented the narcissism and self-interest of fellow citizens, and many of the era’s graphic novels depict rallies, fundraisers, and election campaigns, emphasizing a political system riddled with corruption and inertia. But if some graphic novels identified a democratic deficit in the American polity, Lee Marrs’s The Further Fattening Adventures of Pudge, Girl Blimp (1973–1978) tacked in a different direction, offering a reinvigorated confidence in the ability to achieve progressive goals through political reform, a hope expressed through expansive formal strategies such as swarming panels.“I am prompted by Howell’s wry suggestion of Love and Death in the Comic Novel to explore what the development of long-form comics meant for the articulation of democratic possibilities and dread in 1970s America.”
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.037 |
| Scholarly communication | 0.023 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".