The Dramaturgy of Stage Management: A Constructed Conversation
Bibliographic record
Abstract
I brought three of Toronto’s seasoned stage managers together, at the 2003 Mini-Conference on Dramaturgy, to discuss their dramaturgical contribution to the rehearsal process. The conversation was eye opening to many of the conference participants, some of whom (new and established alike) admitted to never having thought of the stage manager as a creative partner. During the conference session, Naomi Campbell, Shauna Janssen and J.P. Robichaud spoke eloquently about the myriad ways they contribute, far beyond setting schedules, corralling personnel and running shows. It was a potent reminder of how valuable the stage manager can be, particularly on new work Naomi, for instance, was so central to my production of Jason Sherman’s Reading Hebron that I insisted she be credited as the Assistant Director, in addition to her work as the stage manager. Casting your stage manager can be as vital as any other artistic choice. Here are further thoughts, culled from an extended e-mail conversation among the three artists.
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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.021 | 0.039 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".