Exploring information visualization use patterns in casual contexts
Bibliographic record
Abstract
This dissertation describes a series of studies conducted to explore why people use information visualizations during their non-work time (casual InfoVis) and which factors are critical for visualization adoption and long duration use. I also model typical casual InfoVis usage patterns and provide a framework for future hypothesis testing. Each study explored a different facet of casual InfoVis research and each built on lessons from the previous studies. The first study explored the development and evaluation of a casual InfoVis system, PartyVote, and how visualizations can be used to aid informal group social interactions. Results from the evaluation indicate that the system successfully helped give people a more equal share in choosing music during social gatherings and people could strategically choose music, but social pressures did not constrain behaviors or reduce cheating as much as expected. The complexity of factors affecting PartyVote use led to a pseudo-experiment evaluating the appeal of motion based data encoding. Study results indicated that participants formed distinct opinion-based groups and motion data encoding was only considered appealing to less than half of the participants. Utility was a critical factor for half the participants, but a sizable group still preferred motion use, despite knowing that it reduced system utility. My final study examined how people encountered and used visual representations of data (artifacts) during their non-work time. The artifact study led me to develop the Promoter / Inhibitor Motivation Model (PIMM) of casual visualization interaction. PIMM subsequently helps explain results encountered during the first two studies. The model provides a framework for future casual InfoVis investigations and identifies potential shortfalls and areas of concern when conducting casual InfoVis research. PIMM should also help guide future casual InfoVis system designs.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".