Physicalization from Theory to Practice: Exploring Contemporary Challenges for Physicalization Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This workshop aims to delve into the evolving challenges of physicalization, drawing on prior research and workshops to explore overarching grand challenges in the field. Initially formalized within Human-Computer Interaction in 2015, `physicalization' involves encoding data into tangible forms. Despite significant progress in addressing initial challenges, new complexities emerge from the dynamic interplay of technology and human interaction. Building on insights from a prior CHI 2023 workshop, which focused on exemplar domain applications, our workshop aims to facilitate in-depth discussions on overarching grand challenges. Specifically, we focus on four key challenges: privacy, temporality, collaborative sensemaking, and sustainability of physicalization design. These focal points acknowledge the susceptibility of physicalizations to privacy concerns, collaborative interpretation, temporal usage, and sustainability considerations. Through interactive and collaborative activities, the workshop seeks to advance understanding and strategies for addressing these emerging challenges in the realm of physicalization, ultimately contributing to the advancement of the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it