Traveling Arts x HCI Sketchbook: Exploring the Intersection Between Artistic Expression and Human-Computer Interaction
Bibliographic record
Abstract
When thinking of arts in HCI, one might be tempted to keep one’s eyes focused on prominent realms such as sketching for UX Design and design probes from participants. A closer look shows that practices go beyond this, involving a variety of arts-based expressions by researchers, the researched and third parties, e.g. graphic facilitators. Inspired by Toselli’s Sketchnote Army Travelling Sketchbook, researchers and artists contributed to a ’Travelling Sketchbook for Arts in HCI’, showcasing their arts-based practice in HCI. The resulting sketchbook explores the intersection between HCI and artistic expression, illuminating what it means to use art in HCI. It shows the breadth of Arts in HCI, illustrating the many fruitful possibilities for extending existing research and dissemination methods in HCI. It also calls into question current practices, which often do not recognise the significance of artist attribution, and, in turn, advocates for equal authorship between principal researchers and contributing artists.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.006 |
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".