Bibliographic record
Abstract
In this article, I argue that playwriting and performance can act as powerful forms of activism that bridge academic work and public engagement. I analyze my experiences writing and producing a stage production that mobilizes knowledge from my research about queer antiracism in Toronto. This methodological discussion is contextualized within the current political moment that positions work in the humanities as irrelevant and elitist. Performance as a method of knowledge mobilization emerged from interviews with queer peoples and community organizers. I found through conversations with participants that academic forms of knowledge mobilization, such as publishing in peer-reviewed journals, would not necessarily be accessible to community members or appropriate for encouraging discussion and social action at the local level. Participants suggested a range of antiracism organizing strategies, most of their suggestions centered on increasing the understanding and the value of the arts. I decided to meet the challenge of engaging in effective knowledge mobilization that would be in service to the community by developing a stage production called We without You that focuses on the opinions and experiences of participants. I found that producing a stage production based on academic research had powerful social effects that are not possible through traditional knowledge dissemination methods. This article encourages academics to broaden their ideas about effective knowledge mobilization; to position their work as useful and relevant to social issues and as a means of critical resistance against polarization within and outside academia.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.024 | 0.105 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".