Bibliographic record
Abstract
In January 2003, I participated in the Festival of Original Theatre (F.O.O.T.), hosted by the graduate students at the Drama Centre at the University of Toronto. When I arrived for my tech run-through — which, up until that point in my spoken-word career, I’d thought of as the sound-check — I stood onstage while the stage manager called questions from the booth at the top of the theatre. Where did I want my light? What colour of light did I want? Did I want to walk into my light or should it come on once I was already on stage? These were difficult questions for which I had no answers. I didn’t have a director; I didn’t block my performances; I simply went onstage and did my little thing, which, at the time, was reciting memorized personal narrative poems about the intersections of class and gender. I settled on a purple light, I think, which faded at the end of my performance. This was my first time performing spoken word at a theatre festival. After the first show, one of the actors who had listened to my performance from backstage asked me who wrote my script. I wrote it, of course, I told her. It was, after all, spoken word performance and not theatre I wasn’t acting, I told her (a bit indignant, I must admit). I was performing my own words, my own stories.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.052 | 0.017 |
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".