Designing and Deploying Shape-changing Artifacts in Everyday Settings Over Time: Extending Practices and Methodologies for Materiality in HCI
Bibliographic record
Abstract
While the HCI community has developed many unique shape-changing artifacts for supporting novel interactions and experiences, little research has investigated long-term organic experiences of living with these objects. In parallel, Wiberg has proposed the notion of the materiality of interaction as a new ontology for tangible computing. However, cases of designing for materiality are still sparse. The overarching goal of this doctorial study is to fill in the gap between materiality and shape change. To do so, I plan to adopt design-oriented HCI approaches to frame my design implementations and field deployments. Fabricating shape-changing artifacts may extend practices of designing for materiality. Reflecting on design processes and accumulating empirical data of these devices may enrich the understanding of materiality in interaction design and HCI fields.
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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 | 0.044 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".