Cultivating a Cross-border, Extra-theatrical Assemblage in Resistance to Femicide in the Americas
Bibliographic record
Abstract
Abstract: The panamerican ROUTES/RUTAS panamericanas international multiarts festival on human rights (RUTAS) took place at Toronto’s Daniels Spectrum from 27 February to 9 March 2014. Produced by Aluna Theatre—in partnership with Native Earth Performing Arts—the international performing arts festival brought together artists, activists, and scholars from across the Americas. The events discussed in this article—performances and a panel—took place on the final weekend of RUTAS and were part of a cluster organized around the issue of “Femicide in the Americas.” In curating theatrical productions within the context a larger assemblage of performances and conversations organized around the question of Indigenous and human rights issues in the Americas RUTAS overcame two of theatre’s primary challenges—its containment and its positioning of the audience as spectator—dangers that are amplified when staging trauma and when placing narratives of the “other” on display. Further, by extending the horizon of our collectivist accounting to a Pan-American perspective RUTAS also challenged the containment of narratives of femicide and colonialism within nationalistically inscribed borders and in the process disrupted conventionally held notions of Canadian exceptionalism and cultivated possibilities for the mobilization of transborder resistance.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".