On Site with the Electric Company: What Lures Artists out of the Theatre and into the Woods?
Bibliographic record
Abstract
Montreal, July 1991. Dress Rehearsal. It was hot and muggy and the gnats were rising in clouds from the tall grass with every footstep. I was lugging my script, Mag light, pen, jacket, and mosquito repellent up a hill in the failing light, walking the uneven path to the next scene that the audience would walk in a couple days time when we opened the production of Romeo and Juliet that David Hudgins and I were co-directing in Mount Royal Park. I remember thinking, “Who in their right mind is going to come see this show? Only a masochist would give up the comfort of the theatre to traipse through the woods and sit on the — What’s sticking to my shoe? Is that a condom? — ground, swatting crawling bugs off their ankles and out of their eyes in order to watch a play.” Then I reached my destination: a rocky promontory shaded by the arching branches of trees, lit by scattered glowing lanterns, and looking out over rolling hills. Overhead, the leaves rustled in a slight breeze and the sound of city traffic on the Avenue du Pare was a distant background thrum. The dirt floor still held the heat of the day and the scent of dust mixed with that of the decaying underbrush. This was our site for the crypt.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.401 | 0.118 |
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".