Physicalization from Theory to Practice: Exploring Physicalization Design across Domains
Bibliographic record
Abstract
Currently, physicalization research is dominated by technology-centric explorations with limited insights into the broader domain implications. The goal of this workshop is to bring together researchers and practitioners who share an interest in using data physicalizations to solve real-world problems. Hence, we aim to further explore the utility of physicalization for different domains that (already) apply data physicalization in their practices (e.g., sustainability, office vitality, education, and personal informatics). The objective of the workshop is to combine the expertise of researchers working in physicalization and/or exemplar domains to (i) develop an understanding of common challenges, (ii) map out overarching factors across domains, (ii) operationalize design strategies for common domains, and (iv) reflect on the implementation of data physicalizations for different domains. Upon completion of our workshop, we plan to create a BIT Special Issue addressing the challenges and potential directions of the domain application of data physicalizations.
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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 | 0.064 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".