Bibliographic record
Abstract
Critics have often noted that Karen Finley performs in a trance, seemingly possessed by the violent, disgusting and desperate characters she has so famously embodied. According to the Village Voice’s C. Carr, she doesn’t rehearse her pieces but, instead, conducts extensive research and then, when the time comes to perform, turns “inside herself” and “slip[s] into that personalized primeval ooze,” disappearing so completely that she doesn’t recognize herself in videos of her own performances (141). In my own work as a director, I’ve had to rely on something “inside myself” that is not quite the rational, analytic set of skills upon which I usually call to solve problems. Sometimes, a scene isn’t working, and no matter how long I spend pouring over my script or staring at the set trying to fix it logically, it still doesn’t work. And then I have to remind myself to trust that, when I get into the rehearsal space with my actors and stay relaxed and receptive, the answers will come, most likely in a flash of insight at the perfect moment. During these perfect moments, I don’t experience the same kind of dramatic possessions that Carr describes, but I think this difference is one of degree and not of kind.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.068 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".