Bibliographic record
Abstract
This multimedia essay is describing or rather demonstrating one attempt at dealing with the problem how to discuss performance in the mode of performance, based on the presentation at the Performance Philosophy conference in Helsinki in June 2022. There we—four members of the Artistic Research Working Group (ARWG) of Performance Studies international (PSi)—tried to present and demonstrate in a miniature form one of the methods for sharing we have explored in the working group, and to do it in a hybrid format, with two performers in the room and two performers present via zoom. The three phases of the method (perform–respond–expand) were performed by three members of the group and condensed into three minutes of prerecorded material per person at each stage. An extract of our preparatory discussions on zoom is added to the essay, as well as a link to the working group archive or blog.
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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.032 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".