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Record W4321369227 · doi:10.1093/alh/ajac163

Serialization, Solipsism, and Swarming: American Politics and the Graphic Novel in the 1970s

2023· article· en· W4321369227 on OpenAlexaff
Paul Williams

Bibliographic record

VenueAmerican Literary History · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsNovelis (Canada)
Fundersnot available
KeywordsComicsPoliticsDemocracyZombieAdventureAestheticsLiteratureLawSociologyHistoryArtPolitical scienceArt history

Abstract

fetched live from OpenAlex

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.”

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.213
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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