Bibliographic record
Abstract
This thesis is an investigation into the intersection of clown practice and feminist theory. I consider the work of historical and contemporary female clowns and the strategies they use to playfully break rules that restrict and oppress. Through analysis of the ways in which these clowns reconfigure traditional clowning strategies for feminist aims, my research discusses the pleasures and possibilities of a feminist clowning practice. By scrutinising the current literature about clown history and performance, I attempt to fill some of the gaps in this field of scholarly study, writing about the history and practice of feminist clowning and centralizing the voices of other female clowns/scholars. Aiming to address the erasure of women in clowning history, I document my process to revive proto-feminist clowns Cha-u-ka-o and Evetta Mathews, and my Practice as Research experiment, Bridge of Jests, that situates myself in a lineage of feminist clown ancestors. Drawing on interviews with contemporary Canadian clowns Karen Hines, Heather Marie Annis, Amy Lee, Candice Roberts, and Michelle Thrush, I consider the innovative ways that these clowns are harnessing the power of laughter to interact with serious, political content. I discuss the challenges and surprises that emerged from my efforts to develop a feminist clown working methodology during my Practice as Research experiment Allergic to Water. In this thesis I endeavour to draw connections between ways of thinking about feminism and the embodied practice of clown, to discover what emerges as feminist clowning in this interplay between theory and practice.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.047 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".