Bibliographic record
Abstract
In the mid-1960s, when Richard Schechner first began working as a theatre practitioner and educator in New York City, he found himself under the spell of “happenings” created by a variety of young artists in unique locations across the city. In particular, he was captivated by the creations of Allan Kaprow; of which he said, “Kaprow’s work, more than any other I know, has the simple quality of “happening” - of something that is. By ever so slight a change or heightening he converts everyday actions into ‘mysteries’” (Performance Theory 65). The concept of Kaprow’s “mysteries” had a profound effect on Schechner’s thinking about the deep structures present in performance, from around the world and throughout time. Schechner recognized that “happenings introduce us to the idea that art is not a way of imitating reality or expressing states of mind. At the heart of what Kaprow calls a mystery is the simple but altogether upsetting idea of art as an event - an ‘actual’” (Performance Theory 36).
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".