Temporal Forms Unite! A Conversation between Victoria Stanton and Susanne de Lotbinière-Harwood
Bibliographic record
Abstract
One sunny afternoon in July 2006, while chatting on the phone with my friend Susanne de Lotbinière-Harwood, I mentioned that I was working on an article about spoken word and performance art. It would be an exaggeration to say I was stuck, but I was certainly talking (more like writing) myself in circles. This is when Susanne suggested we meet in person to continue this conversation, just to see if we could “un-stick” what it was I was trying to say. Susanne, a performative lecturer and literary translator, is infinitely interested in process, in process-based and time-based arts practices and how these interact. We also have a history — not only of discussing such topics but of actually collaborating on performance pieces. What follows is a transcription of our discussion: the recorded performance of the interview process. Instead of synthesizing my thoughts with Susanne’s questions to produce a seamless whole (where the interviewer is markedly absent), together we decided the interview — left intact — more authentically represents our approach to these issues and is more aligned with our respective research, academic and performance practices.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".