“Well Intended Liberal Slop”: Allegories of Race in Spiegelman's <i>Maus</i>
Bibliographic record
Abstract
In a 1992 interview, Art Spiegelman described the genealogy of Maus , his acclaimed comic-book treatment of the Holocaust. He was inspired to write Maus , he stated, when asked to contribute to a commix anthology called Funny Aminals ; the only restriction on his creativity was that the story must somehow involve anthropomorphized animals. “At the time I was trying to figure this out,” Spiegelman reports, I went to sit in on some classes of a friend of mine, Ken Jacobs, a filmmaker and very wonderful teacher at SUNY Binghamton, who was showing some old animated cartoons in his class with cats and mice romping around, and then he was showing some racist cartoons from the same period, and it became clear that there was a connection between the two, that Al Jolson was Mickey Mouse without the ears. At that point I said, “I have it: I'll do a comic-book story about the Ku Klux Kats, and a lynching of some mice, and deal with racism in America using cats and mice as the vehicle.” And that lasted about ten minutes before I realized that I just didn't have enough background and knowledge to make this thing happen well, that it would just come across as well intended liberal slop. And instantly the synapses connected, and I realized that I had a metaphor of oppression much closer to my own past in the Nazi Project. (Spiegelman CD-ROM)
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.038 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".