Bibliographic record
Abstract
While internet performances may, at first glance, seem to lack the bodies together in a space that has been considered a hallmark of popular entertainments, a critical look at new media performances reveals a strong link between them and traditional popular entertainments. In addition, changing notions of the body, liveness, and space have complicated even the idea of what “live bodies interacting in real space” in fact means. These concepts have been preliminarily explored in relation to websites such as YouTube, but newer sites like Stickam challenge notions of liveness and the body more clearly. With the ability to interact with multiple viewers over webcam in the same “room” simultaneously while watching the main performer or performers, Stickam creates a live as well as mediatized space for entertainments that both does and does not contain live bodies. The short acts linked together into performances intended for the people, as found in the tradition of popular entertainments, are now frequently a component of new media performances. Danielle I. Szlawieniec-Haw is a professional actor and writer. She is also a PhD candidate in Theatre Studies at York University, Ontario, Canada where she is pursuing her studies into the effects and ethics of representing trauma.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".