How to Remediate; or, Gertrude Stein and Virgil Thomson’s <i>Four Saints in Three Acts</i>
Bibliographic record
Abstract
In this article, I explore the art form of opera as a process of remediation, a particular type of intermediality in which one medium is represented in another. Focusing on the visual domain, Jay David Bolter and Richard Grusin argue that remediation dominates digital media today. The Stein–Thomson collaboration is a fascinating precursor not only because of the writer’s and composer’s shared attentiveness to experimentation within and across media but also because of their mutual interest in complicating presumptions about aurality. By investigating Four Saints in Three Acts as remediation, we learn how the media of language (visual, oral, and aural) and sound (musical and performed live) participate in intermedial negotiations – and how, when media are combined for and in performance, they act on each other.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".