Design and semantics of form and movement 5:DeSForM 2009, October 26-27, 2009, Taipei
Bibliographic record
Abstract
The face of design is changing.The products we use increasingly become part of larger systems that connect multiple people with multiple technologies.This emerging field of 'systems design' presents design research with new challenges.How can design research deal with the complexity of multiple people and multiple products that are intricately connected?In this joint presentation, we explore systems design with a focus on the question of how to create meaningful interactions.This question is addressed from a specific theoretical point of departure that looks at action as generator of meaning in human-product interaction.Respect for all human skills, e.g.perceptualmotor, emotional, cognitive and social skills, is key in our approach.We observe that current systems design tends to resort to abstractions to deal with the complexity of systems (think of on-screen social software for example).We see much potential for creating meaning in human-system interaction by capitalizing on human skills in a more physical way.The projects 'Rich Interaction' and 'Ethics & Aesthetics in Interaction' are presented to illustrate our research approach, and to lay the groundwork for our venture into systems design.Both research-through-design projects feature innovative designs that allow people to meaningfully interact with products through expressive, physical action.Despite the fact that these projects stay in the domain of one product-one person interactions, they provide valuable insights for the creation of meaning in systems design.We present a set of systems design explorations based on what we learned in our previous research.These designs share the intention to capitalize on all human skills, including the physical.By reflecting on them, a number of new issues, insights and questions for systems design emerge.These reflections provide researchers with refreshing considerations for moving into the field of systems design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.013 |
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".