All That Moves Us: The Semantic Density of Clothing and Objects in <i>El Buen Vestir-Tlakentli’s</i> Choreographies of Indigenous Movements
Bibliographic record
Abstract
This article explores El Buen Vestir-Tlakentli 2 (2017 and 2019) and argues that the two iterations of this dance-theatre piece are powerful explorations—through dance and the artists’ sensate relation to objects—of the moving journey that led artists Leticia Vera and Carlos Rivera Martínez from Mexico to Canada and from a sense of estrangement from their Indigeneity to an encounter with their ancestors’ complex and resistant negotiations with the oppressive forces ushered in by settler colonialism in Mexico. Shedding and layering clothes and leveraging the knowledges they encode, the artists remind us of all that moves us in the world. Through inhabiting clothes saturated with meaning, the dancers embody their ancestors throughout the performance, returning to them to better understand the past, its violence, and its joy and imagine a future remapped through decolonial geographies. This future, the author contends in conversation with the artists’ own words, takes the form of a hopeful reconfiguration of the symbolic space of Aztlán. Critical of its problematic past entanglement with race yet mobilizing its transformative power, the artists create a space where Indigeneity is no longer relegated to the past, where kinship is articulated on radically different terms, and where it might be possible to shed the layers of settler-colonial violence—gendered violence in particular–that continue to impact the everyday life of Indigenous women and girls.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".