Bibliographic record
Abstract
Through my subjectivity as a non-binary, trans* vocalist, I am acutely aware of the sounds I have been enculturated to produce.Drawing on theories of vocality (Eidsheim 2019; Azul 2015), transition (Constansis 2013; Constansis and Foteinou 2017), and improvisation (Caines 2021), I analyze the relationship between voice and perceptions of gender by exploring the concept of transition as it is performed by my voice during the first seven and a half months of testosterone treatment (mid-June 2021 -January 2022).I contextualize my experience within historical and medical notions of gender and examine it through autoethnographic methods and creating an experimental music video, Trans*Vocal.I open up and make myself vulnerable in the hope that greater knowledge of trans* experience will bring appreciation for "non-normal" vocalizations.I aim to problematize our current language surrounding gender, voice, and transition and depict transition as a natural part of anyone's vocal journey.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".