Bibliographic record
Abstract
In Marie Brassard’s most recent play, Peepshow, there is a scene that, in my view, gives insight into the nature and the spirit of her theatrical process and of the work she creates. In this scene, a young girl describes an experience she has had in school. Her teacher shows a flashcard on which a word is written, and she is to determine whether the word is best categorized as a “person” or an “animal.” The word she is shown is “Dad.” The little girl decides that person is too simple and obvious and that this must be a trick question. The less obvious answer must be the right answer. “Maybe the teacher knows something I don’t and she’s going to show it to me,” the little girl muses. “Maybe she’s going to open a door to a world I don’t even know exists. And at the end of the day, I’ll be a better person.” With this in mind, the little girl responds, “Animal.” Disappointedly, her teacher tells her that she is wrong, and relegates her to the side of classroom reserved for “not-so-clever ones.” “The next day at school I understood the game,” she tells us, “and from then on, when I was asked a question, I’d answer the obvious answer. And everyone was happy, except for me. Because deep inside I knew I was right. I knew there must be a door somewhere” (Brassard 3–4).1
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.033 | 0.019 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".