Bibliographic record
Abstract
The Talking Creature was the inaugural event held by my theatre company, Mammalian Diving Reflex, in our new program, SocialCapital. SocialCapital is a wing of the company dedicated to stripped-down research, experimentation, discussion and artistic forms that, as yet, remain off the radar of traditional theatre and performance practices. The Talking Creature was an experiment in trying to isolate two core elements in the theatrical experience: talking and strangers. Standing in front of an audience of people you don’t know or, at least, don’t know very well and establishing an open channel for the transmission of ideas is, in my experience, nerve-wracking. There is a tendency to imagine the audience is thinking the worst, that they are aware of your every mistake and are there to judge you as harshly as you judge yourself. Or if, on the other hand, you happen to have an overabundance of confidence, you run the risk of trying to dazzle, and this also rules out an open conduit of communication. I confess to oscillating between these two tendencies. The Talking Creature required humility, confidence, talking and listening; arguably, the four cardinal points in almost all theatre.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".