Big Daddy Lives or Don’t Say the F Word: Intersectional Feminist Directing in Theory and in Practice
Bibliographic record
Abstract
As a theatre and gender studies double major at the University of Victoria, I have been ableto critically think about the ways each of my fields of study could benefit the other. In myexperience, many courses in the UVic Department of Theatre generally focus on dramatic texts andtheoretical literature written by white men. Consequently, contributions to the theatre by women,people of colour, and/or non-Western theatre practitioners are largely dismissed or ignored. Myfrustration with this pattern was what led me to create Big Daddy Lives or Don’t Say the F Word,a part scripted, part devised performance piece that staged scenes from classic and contemporaryplays using directing theory written by feminists, for feminists. I curated the excerpts, wrote thetransition-text, and directed the play using an intersectional feminist framework. The project wasan experiment in applying intersectional feminism to theatre directing in order to critique the waythe male-dominated canon of plays and theories shapes theatre education. Through this project, Ifound that intersectional feminist directing techniques foster collaboration; encourage discussionand mutual education about identity, oppression, and representation; and can be applied to theproduction of both classics and contemporary feminist plays and to the creation of new work by anensemble.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".