Bibliographic record
Abstract
This paper is both a theoretical and creative exploration using fan ficion. Monsters have drawn my interest because they are often metaphors for marginalized folks. Through histories of marginalized experiences represented as monsters and villains, I claim the monster as my own. In more recent iterations of the monster, I have observed this pull towards the normate looking at the show Teen Wolf in comparison to the 1984 movie by the same name. The monster becomes the protagonist, but in doing so, ends up becoming predominantly white, heterosexual, cisgender, abled, thin, and conventionally attractive. Furthermore, the representations of the monster consist of bodies that draw closer to the normate, but are exemplary of the norms of desirability. In short, they find the hottest models to play as monsters. The monster is no longer the marginalized subject, but becomes an expected, unattainable norm of desirability like Audre Lorde’s “mythical norm”. In response to this mythical norm, I have rewritten the scripts as fans sometimes do. In Teen Wolf, the protagonist, Scott McCall becomes abled upon becoming a werewolf. What if he stayed disabled and wasn’t drawn closer to the normate? What if instead, he stayed a disabled nerd and ended up in a relationship with his best friend, Stiles, another disabled nerd? This little slice of life explores a little about what it’s like to be disabled, queer, racialized, and a monster that’s a little more representative of what it’s like to be marginalized.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".