Staging the Internet: Representation (Bodies, Memories) and Digital Audiences
Bibliographic record
Abstract
If, as Jon MacKenzie writes, the world is now a “designed environment in which an array of global performances unfold,” what does performance designed for that most global of stages, the Internet, look like? This shift has caused me, as a scenographer, to consider how what I do is made complex by understanding that stage and audience are now globalized. MacKenzie suggests that feelings and affects are dispersed globally through social networking practices that communicate with a speed and intensity never before known. Here, such an experience is foregrounded: The Wilderness Downtown exemplifies the affective and globalized turn in interactive performance designed for the Internet and meant to engage each viewer “where they live.” The Wilderness Downtown elicits, through memory and sophisticated technology, a global feeling that is immensely “affective.” It succeeds in creating an awareness of being both local and global, unique but interconnected. Is this the future of scenographic representation?
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".