Performing Technology: Mapping Interface Metaphors and Interactive Dramaturgies
Bibliographic record
Abstract
Digital technologies are not dramaturgically neutral. Rather, when we make the choice to use a piece of technology in performance practice, the technology has already made a series of choices for us. These choices manifest in the interface metaphors that define how we interact with digital technology and are deeply embedded in the developmental ancestry of each device.Given the profound impact of computational systems on performance, it is critically important to question when those choices are made, by whom, and to what extent, if any, computer systems make or define users’ dramaturgical choices. This process is rooted in digital dramaturgy, an interdisciplinary approach that combines intermedial-performance study, media theory, history of technology, and hands-on experimentation to establish a hybrid critical framework with a practice-based methodology which directly engages with computer hardware, software, and wetware. Through a series of case studies presented over five chapters, this dissertation examines several prominent contemporary technologies used in performance, including digital projection systems, media servers purpose-built for performance use such as TroikaTronix’s Isadora, Figure 53’s Qlab, and livestreaming studio Open Broadcaster, as well as playful appropriations of the Xbox 360 Kinect motion tracking camera and videoconferencing platform Zoom. These digital technologies use non-linguistic metaphors, including images, sounds, gestures, sensors, and other techniques which relate the logic and structure of prior (often analog) systems within the current (digital) technology. As such, this research draws on George Lakoff and Mark Johnson’s notion of conceptual metaphors, which recognizes that metaphors are not merely linguistic devices, but systemic cultural expressions which humans use to communicate ideas and concepts on a structural level. In each case study, this research aims to identify what metaphors are present, how they are constructed, and most importantly, assess how they drive what is created using that technology. This thesis finds that modern digital technologies embedded technical or creative dramaturgy. When used within a performance, these technologies complicate power dynamics, foster specific ways of doing and thinking, and even enforce rigid modes of production. By mapping and revealing how these technological systems operate, this research hopes to encourage exploring and building new alternatives.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".