<i>Speculative Scores</i> | Moving Together, 22 Ways
Bibliographic record
Abstract
In Moving Together, 22 Ways, Justine A. Chambers and Alana Gerecke offer twenty-two choreographies of the everyday, each an invitation for other bodies to take up and explore. These choreographies approach assemblies in motion, itinerant assemblies that spread and gather before they have time to settle. The assemblies these scores propose are speculative choreographies worked through the flesh. They register relationships to the built environment and to the bodies that populate those environments as fleeting and kinaesthetic, embodied knowledge on the move. Drawing from their respective practices and fixations, Chambers and Gerecke take up the choreographic invitations built into the architecture of specific places: Canadian sidewalks, transit platforms, intersections, elevators, and public squares. They insist: if objects choreograph us, and if the city functions as an architecture of assembly, then it is worth thinking seriously—and kinaesthetically—about how the objects that constitute our public spaces move us and shape our interactions. The scores engage with unspoken movement expectations that structure these places by offering subtle shifts to the formal arrangements of assembly each site invites. In other words, these twenty-two scores reframe and rework the dances that are already there, the social choreographies found all around.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".