Whither Trans/Queer Music Studies?
Bibliographic record
Abstract
In this paper, the authors develop a critical dialogue to trans/queerly trouble paradigmatic directions in music studies. Particularly, the authors engage the liberal tendency to celebrate trans/queer interventions as a discipline’s progress towards becoming more diverse and inclusive. The article emerges from a recent panel at Feminist Theory and Music 17 in Ann Arbor, Michigan. Through wide-ranging engagement with music and sound, we attempt to (re)orient our relationship to music studies and its various interventions, including the (im)possible emergence of trans/queer as a distinct area of study. The authors collectively draw on mutable forms of embodiment to explore trans/queer music studies otherwise—that is, to pursue a relational mode of scholarship that provokes generative and capacious directions in our study. Recognising ourselves within the ‘terrible we’ of trans scholarship (Awkward-Rich, 2022. The Terrible We: Thinking with Trans Maladjustment. Durham: Duke University Press)—as junior, racialized, (dis)abled, trans/queer scholars—how do our relations on and off the page revel in loudness, noise, and dissonance? Simultaneously, we locate these trans/queer relationships within contemporary material histories, including the regulation of bodies through anti-trans/queer legislation, relational modes of performance and composition, and Brazil’s history of slavery. We might imagine this article as a discordant chorus more than a harmonious gesture of calcification, which is to say that the voices in the article indulge in a dialogic cacophony in order to trace (dis)connections between the fractal nodes of trans/queer critical thought in music studies. While it may be comforting to proffer a legible response to recent developments in trans/queer music studies, we instead suggest that a critical hesitancy might better afford an overdue reflexivity for trans/queer music scholars. As such, the essay invites readers into the ‘terrible we’ in hopes of grounding our work in contemporary material quagmires to open toward possibilities of trans/queerly musicking otherwise.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.055 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".