FlexPhys: A Workshop Cookbook for Operationalizing Data Physicalization Research Questions
Bibliographic record
Abstract
We introduce FlexPhys, a cookbook that researchers can use to operationalize data physicalization research questions through workshop design. While guidelines exist for running workshops in educational contexts, designing a data physicalization workshop when the goal is to answer research questions is an ad-hoc process for which little guidance exists, but for which many choices must be made (e.g., in terms of materials, tools, and data). We draw from our experience designing data physicalization workshops and from reviewing three existing workshops, to distill the cookbook's core ingredient (context and goal) and eight additional ingredients related to making (material, tool, technique), data encoding (data type, variable, mark/unit), and interactivity (interaction, sensory modality). We then show how FlexPhys can be used to describe and compare physicalization workshops, and to generate workshops that address specific data physicalization research questions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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 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".