Unjuried, Uncensored, Uncurated: Rethinking Canadian Fringe Festival Mandates in the Twenty-First Century
Bibliographic record
Abstract
This article explores recent conflicts surrounding the mandate that Fringe Festivals be “unjuried, uncensored, and uncurated.” While this concept of “anyone can do anything, and everyone is welcome” is exciting in theory, recent practical issues have demonstrated how challenging it is to follow this ideal in reality. For example, giving a homophobic show a space in a festival can make that festival less welcoming for queer performers; being inclusive of discriminatory content can actually make the festival effectively less inclusive. In an example of the paradox of tolerance, being truly tolerant of everything is impossible, lest one end up tolerating intolerance and thus enabling oppression. This article focuses specifically on the Montreal Fringe Festival and the techniques employed by its artistic director (and Canadian Association of Fringe Festivals President) Amy Blackmore to respond to this conflict. Dedicated to both preserving artistic freedom and prioritizing community care, Blackmore’s community-centred approach is explored as a promising way to navigate this major issue currently facing Fringe Festivals.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.049 | 0.041 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".