Make It Pink: Superman, Pink Kryptonite, and Fandom
Bibliographic record
Abstract
This paper explores several representations of pink kryptonite. In Doug Murphy’s animated short film True Colors (2017), Superman (Jason J. Lewis) transforms into a woman when he’s exposed to this substance. This short, as we have shown elsewhere (2025), offers a useful lens for examining the perpetuation and the critique of gender stereotypes in superhero media: Superman may be equally capable regardless of his sex, but the film pokes fun at, rather than celebrates, his transformation. Pink kryptonite rarely appears in the canon. A single panel in Supergirl (2003) has received perhaps the most sustained attention. Tom Ue’s archival work at the Library of Congress has revealed but one more instance in comics, in an issue of Superman’s Pal, Jimmy Olsen (2019). In both cases, exposure to the substance makes Superman attracted to Jimmy, and they leave us with more questions than answers: That Jimmy does not reciprocate the superhero’s attentions makes all the more apparent the power imbalance in this sexualized relationship. In this paper, we argue that pink kryptonite can be a useful catalyst for initiating all kinds of critical discussions regarding gender norms and that this conversation has been continued by fans. Through close analysis of canonical and fan treatments of pink kryptonite, this paper weighs in on both the potentials and the limitations of canonical texts; and it argues for the value of investigations in libraries and information repositories.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".