Bibliographic record
Abstract
Dance curators (or programmers, as they are often called) have a significant impact on the dance field throughtheir selection processes: elevating certain works, practices, and artists, while effectively excluding others. Through this, they have a considerable hand in shaping what kinds of dance pieces a local audience has access to,effectively writing dance history over time. But their working processes remain poorly understood, and there have been limited attempts to theorize their practice. This article begins with an exploration of the etymology of the term curator and the historical emergence of the curator in both the fine arts and dance. It then goes on to examine the role of the curator as mediator in two common models for dance presentation (the festival and the theater season) and explores two alternative curatorial models (the focus program at Brussels venue Beursschouwburg and the uncurated model of Amsterdam festival Come Together). Finally, it explores the practice of dance curation as a form of choreography itself. It concludes that contextualizing dance curation asa form of choreography could be an effective starting point for theorizing the practice, hopefully paving the way for further study.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".