Bibliographic record
Abstract
I am sitting at a table in a popular North End diner in Halifax, looking at a menu for Café DaPoPo, a theatre event created by Garry Williams, when an actress comes up to me and asks me if I am ready to order my performance. “Just to let you know the specials are listed on the back of the menu,” she says, “and we have no one act play tonight.” As a regular theatregoer, I feel rather confused. On the one hand, I have never ordered my theatre before, so I cannot pretend to know how to do it. However, the menu clearly lists the scenes as starters, mains, and deserts, and I must confess that I am a big fan of food. The fact is, I have never had to make this kind of choice. Not only am I taken out of my normal, comfortable position of audience member; I already sense an emerging theme to the evening — the role of the spectator in the hybrid performance space — in which I am going to be involved. Suddenly, another actor begins intimately reciting a Shakespearean sonnet for a couple seated at a table in the corner. He is whispering into their ears, and it looks rather enticing. “I’ll have one of those to start,” I say. The actress replies, “Would you like to add a German hand puppet to that for an extra dollar?”
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.717 | 0.496 |
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".