A+ COMICS! an anarchistic, autoethnographic approach analyzing and adapting an absent, abject, AMAB abomination (aka ABOMINATRIX); and also, \naccommodating an author’s adult ADHD & anxiety awaiting an apocalypse… academically approved, ACAB.
Bibliographic record
Abstract
Morgan Sea went to grad school to make comics and avoid being ground into the dirt by capitalism. \n \nWhile trying to understand Kristeva’s theory of the abject, her magical adhd brain made a tangled web of connections between obscure marvel comics, trans misogyny, and the monstrous feminine. She wrote and illustrated a 14-page comic about a transsexual, legally distinct She-Hulk type character named the Abominatrix. Attempts at writing and illustrating another comic about mermaids were derailed by an extended depressive episode, so Sea doubled back and handwrote an experimental thesis exploring the creation of her Abominatrix comic. \n \nThe results are handwritten autoethnography of process, formal and informal research, cartooning experimentation, and mental illness navel gazing. This thesis is a piece of performative research where documentation of process combines with the original art to create another art object. \n \nThrough this research, Sea was able to experiment with cartooning and find her writing and illustrative voice, build a portfolio, and survive a few years in our late capitalist hellscape. \n \nTAGS FOR THE ALGORITHM: \n \nIdentity politics: Transsexual, transgender, trans art, saphic, bisexual, Saskatchewan born artist, colonizer, canadian, trans woman, gay, lgbtq, lesbian, t4t, w4w, very attractive woman, some kind of genius, just gorgeous and funny too. \nMethods: Cartooning, illustration, writing, comics, cartoonist, autoethnography, autobiography, graphic medicine, adhd, anxiety, depression, powers of horror, abjection, capitalist realism, trans feminism, tumblr, \nAdvisors: Fiona Smyth, Dr. Michelle Millar and Shannon Gerrard \nArtists that vaguely have to do with this project: Lynda Barry, Joe Sacco, Jack Kirby, Steve Gerber, Buzz Dixon, Stan Lee, John Buscema, John Byrne, \nThings Disney owns: Hulk, She-hulk, Fantastic Four, Abomination, mutants, most superheroes, Spider-Man.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".