Designing the XR Medium: Five Rhetorical Perspectives for the Ecology of Human+Computer Networks
Bibliographic record
Abstract
<p>Cycles of invented digital media environments driven by the optimization of feasibility of media technology, from personal computers to social media, while creating efficient systems, have also produced psychological and social side effects including media addiction, depression and anxiety among users, and erosion of privacy and fraying of the social fabric. Escalating blending of immersive technologies with advanced computation allows system makers to produce not only human-computer interaction networks, but advanced, multi-minded human+computer (H+C) systems. The critical shift toward user immersion within systems of digital information and simulation makes the scale of immersive media’s potential impact on human life, culture and well-being unlike that of any previous medium. This dissertation addresses H+C immersion as a multi-dimensional design problem—a Research Through Design (RTD) zone which addresses the question: How can design-thinking-based knowledge system complement the existing human-computer interaction (HCI) invention model to contribute to the creation of more socially desirable and human-centred immersive media environments? The dissertation positions human+computer immersion design as a field of rhetorical influence, which starts with the initial design of the H+C system and continues as a mindful dialogue between system designers and users, once the system becomes active. The dissertation aims to make its contribution to both design and media theory by applying design thinking paradigm to the human+computer immersion problem by proposing a framework of four test and application ready graphical models intended to allow immersive media makers from the engineering backgrounds to adopt a user-centred design approach while encouraging user experience (UX) practitioners from design backgrounds to enter the field of immersion experience design. It leverages the Inspiration-Ideation-Implementation design process to propose the logic model for H+C immersion design. Inspired by Bateson’s call for the ecology of mind, it also offers 35 axioms for the ecology of H+C systems that attempt to pragmatically unite the human and the computer perspectives of socio-technical systems. Additionally, it leverages Gibson Bond’s game design framework to offer the layered H+C immersion design model. Finally, it offers the proposal for five rhetorics of extended reality experience design (XRX) that blend the discursive and non- discursive approaches to multimodal rhetorical media composition.</p>
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".