Bibliographic record
Abstract
A good deal of dramaturgy is focused on content. A discussion of whether or not a production is successful will often revolve around the quality of the play: whether the premise, themes or characters are credible; or perhaps whether the story was worth the effort of production. If the focus shifts to the production, there may be conversation about the casting, acting, directing, or design. On rare occasions, serious consideration may be given to the program for the production or perhaps to how the show was promoted to the public. All of these are very good points of discussion about any performance in the theatre; however, there is rarely — if ever — any discussion about form: Why did this performance begin with a play? Or, why did the performers play characters? Why did a performance happen in a theatre, as opposed to some other place? Why perform for a live audience? All are possible questions that seem rarely asked, perhaps because they are considered beside the point… . But why? If we can agree that most theatre productions, on one level or another, are manifestations of the dreams of those who create them, and that, therefore, theatre is a space of public dreaming, it might be a really good idea to consider why our “dreams” assume the form they do.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.164 | 0.101 |
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".