Collaboration Dynamics in Constructive Physicalization of Shared Personal Data
Bibliographic record
Abstract
There is evidence that constructing a data physicalization can empower a diverse audience to engage in data understanding and decision making. Although the hands-on nature of data physicalization construction enables a familiar way for people to collaboratively construct and interact with representations, we do not know how this collaboration happens. We conducted a qualitative study with 12 participants (six couples) to better understand how people collaborate to construct a physicalization, using a combination of elicitation diary and semi-structured interviews over a period of several weeks. We found that participants employed three main styles of collaboration - mutual, exclusive and dictator, when working together to construct a physicalization. We found that such collaboration styles are facilitated through actions such as discussion, distributing tasks, and preparing and assembling tokens. Informed by our findings, we discuss: i) similarities and differences between previously proposed physicalization workflows in the literature and ours, and ii) research directions to foster collaboration in data physicalization processes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".