On the Threshold of Becoming: Transgender Monstrosity in the Music of Kim Petras and Arca
Bibliographic record
Abstract
Monsters have always existed.For as long as humans have formed societies there have been monsters lurking on the outskirts; a shorthand for everything dangerous, different and unknown.The figure of the monster defines the outer limits of normal; demonstrating when unusual becomes unacceptable and showcasing the revulsion and rejection awaiting wretched creatures who cannot conform.Given this context, it is little wonder that a marginalized group of people would resonate with tales of monstrosity.In "My Words to Victor Frankenstein above the Village of Chamounix" Susan Stryker likens her experience as a transgender woman to that of the Creature in Mary Shelley's Frankenstein, a being forced to fumble blindly through a society that is actively hostile to its existence.In this thesis I use Stryker's monstrous affect of transgender rage as a starting point to explore how trans artists Kim Petras and Arca embrace a monstrous aesthetic in their music.I examine the role of the monster in society and in fiction, outlining the queer interpretations of several common archetypes before more closely analysing the monstrous trans affect present in the music and imagery of Petras and Arca.Petras' Turn Off the Light mixtapes draw from the taboo desires of gothic horror, while Arca's Kick quintet centers the uncanniness of alien and cyborgian entities.Both artists present strong figures that deviate from societal expectations due to their assertive sexualities and trans identities, a deviation that earns them the title of monster.This thesis aims to highlight the distinctly transcoded nature of their monstrous personas and frame their music as a continuation of the preexisting practice of queer people finding strength in reclaiming monstrosity. AbrgLes monstres ont toujours exist.Ds la formation des socits, des monstres apparaissent en priphrie, symbolisant le pril, la diffrence et l'inconnu.En incarnant ce qui dpasse l'ordinaire, le monstre met en vidence la frontire de l'acceptable, dmontrant comment la diffrence devient motif de dgot et d'exclusion envers ceux qui ne peuvent pas se conformer.Il n'est pas surprenant que des individus marginaliss s'intressent aux rcits de monstruosit dans un tel contexte.Susan Stryker, dans My Words to Victor Frankenstein Above the Village of Chamounix , assimile son exprience de femme transgenre celle de la Crature de Frankenstein dans le roman de Mary Shelley, condamne avancer l'aveugle dans une socit qui rejette sa prsence.Dans ce thse, j'utilise l'affect monstrueux de la rage transgenre
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".