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 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 | 0.047 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.002 | 0.002 |
| 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".