Bibliographic record
Abstract
When the editorial consortium of Performance Matters first conceived this special issue on “Performance and Pedagogy,” I had no idea that its preparation would coincide with one of the most transformative teaching experiences of my academic career. This past spring semester I cotaught a graduate seminar with Dara Culhane, my colleague at Simon Fraser University, and the Associate Editor of this journal. Our goal was to combine theories and methods from performance studies and sensory ethnography to investigate various embodied sites of research and ways of knowing as they are increasingly practiced across a range of academic disciplines, including anthropology (Dara’s departmental home), literature and the fine and performing arts (between whose units I teach), and gender studies (where Dara and I both have faculty affiliations). In the end, our biggest challenge lay not in soliciting support from our respective program chairs (we did so fairly easily, and with surprising enthusiasm for our initiative), nor in getting the required enrolment (we were oversubscribed), nor even in convincing our students to interrupt their discussions of a given text to engage in some breathing exercises, or a game of Simon Says (they were all eager and willing participants). Rather, the greatest irritant was figuring out how to cross-list the course across three different units, a performative impediment our university’s information management system proved singularly ill-equipped to handle.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".