Everyday Choreographies with Alana Gerecke and Justine A. Chambers
Bibliographic record
Abstract
In “Everyday Choreographies,” Alana Gerecke and Justine A. Chambers examine the city as a choreographic force. We take seriously the notion that precisely how bodies are moved, organized, coordinated, and composed has direct bearing on the possibilities and limitations of spatial as well as social engagements and orientations. As such, we explore how the city — this city — directs everyday trajectories, shapes movement vocabularies, disciplines bodies, and choreographs the social along specifically classed, raced, gendered, ableist and other lines. We experiment with how the emplaced and embodied gaze functions to expose and create patterns and constellations of bodies in motion, generating a set of gentle choreographies. We also pause over some recent moments of choreographic reorientation as articulated by professional dance artists in and around Vancouver.\nChoreography Walk\nWe ask, how does the city orchestrate a set of quiet, everyday choreographies? How are dance artists refiguring these mobile arrangements? How does the project of choreography — of directing bodies — resonate with broader ethical concerns about moving and being moved? Experimenting with a practice-based approach that will get us all moving, we seek to reorient understandings of the role of social choreography in “place-making” initiatives, community creation, and the formation of temporary publics.\n
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".