Transformational Voice Work: Using Theatre-Based Vocal Practice to Support Gender-Diverse Voices
Bibliographic record
Abstract
As a voice coach and teacher, Jane MacFarlane guides actors to a greater understanding of how their voice works, to reveal a character that is rooted in truth and believability. The foundation of the work she does is based on Kristin Linklater’s Freeing the Natural Voice, which identifies habits and patterns of tension in the body that can limit the range of our voices and employs techniques to access the fullness and richness of the vocal qualities we are born possessing. A few years ago, MacFarlane was approached by a colleague who was transitioning and wanted to know if voice work could help her find a vocal quality that affirmed her gender. They began crafting a progression through the voice work that helped her find a voice that felt organic and true to who she really is. Through working with her on her journey, MacFarlane became associated with Skipping Stone, a transgender support organization in Calgary. Together, they began to offer online classes that were based on the work MacFarlane does with theatre practice and applied it to the needs of the transgender community. This article outlines the process of the eight-week progression for vocal feminization through Skipping Stone.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".