C reating a C ontemporary A rchive : A lonzo K ing L ines B allet at T wenty -F ive
Bibliographic record
Abstract
Abstract: In the title essay from Choreographing History , Susan Leigh Foster interrogates the belief that the body serves as a site for dance to be enacted upon and through. She, along with several others in this edited volume, sought to reposition and consequently enhance the contribution of dancers’ bodies by not limiting the essays within to observational accounts or choreographic reviews. Instead Foster and her colleagues query “the possibility of a body that is written upon but that also writes” as means of urging dance scholars to “move critical studies of the body in new directions.” I take this position as an entry point and use it to contemplate the process of making and writing about contemporary ballet history using Alonzo King LINES Ballet, a San Francisco–based troupe, as a case study. In this essay, I describe the challenges I and others have faced in our efforts to collect and interpret a visual and verbal archive for this dance troupe, which has been performing for more than a quarter of a century. This process highlights a larger issue for dance historians, namely, how to create a critically sensitive archive for a company when the most reliable (and oftentimes the only available) source is the personal memory of its choreographer. I anchor this analysis to the different models of critical writing about bodies offered by Michel Foucault, Roland Barthes, Jacqueline Shea Murphy, and Hayden White and suggest that companies such as LINES offer challenges to those engaged in the archival process and in the practice of writing history.
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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 | 0.005 |
| 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 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".