The power of the marvel(ous) image: reading excess in the styles of Todd McFarlane, Jim Lee, and Rob Liefeld
Bibliographic record
Abstract
During the 1990s, Todd McFarlane, Jim Lee, and Rob Liefeld generated record-breaking comic book sales and reshaped the entire North American comics industry by co-founding Image Comics. Despite this tremendous popularity and influence, the only scholarly writing on any of these creators is a chapter about Liefeld in Bart Beaty and Benjamin Woo’s The Greatest Comic Book of All Time, which partly focuses on this very neglect. It is understandable that a comics studies field still anxious to be taken seriously would reject the excessive and sometimes technically limited styles of these three creators. Yet excess is a meaningful mode of representation in its own right, worthy of serious investigation. Focusing on McFarlane, Lee, and Liefeld’s breakout work for Marvel and taking inspiration from both Beaty and Woo’s work as well as Scott Bukatman’s argument that superheroes represent ‘a corporeal, rather than a cognitive, mapping of the subject into a cultural system’ (2013, 49), this article will examine how these creators’ excessive superhero bodies reflect tensions underpinning the image-focused culture of the 1990s that was ultimately responsible for their fame and fortune, and with it, a major shift in the balance of power within the American comic book industry.‘The Power of the Marvel(ous) Image: Reading Excess in the Artwork of Todd McFarlane, Jim Lee, and Rob Liefeld’
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".