Motivation and Procrastination: Methods for Evaluating Pragmatic Casual Information Visualizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For casual users, how do goals and incentives interact with visualization usage patterns? Professional race car drivers are almost exclusively concerned about a car's performance, whereas average car owners might be swayed by fuel efficiency, aesthetics, and even color. Similarly, factors other than performance might motivate casual information visualization (InfoVis) users. Outside of work contexts, visualizations serve as cognitive aids, art, propaganda, and even procrastination aids. Out of curiosity, we asked two women with no computer science training to look at the digg visualizations by Stamen design. To our surprise, comments changed from "sooo cute" and "I like [the] animation" during the first minute to "annoying" and "cute but not practical" less than five minutes later. Motion rapidly went from being appealing and motivating to being distracting and discouraging. Perhaps simply getting eyes on the screen is insufficient. But what makes a visualization successful in informal contexts, and if we do not know, how do we find out?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it