Phasking on Paper: Accessing a Continuum of PHysically Assisted SKetchING
Bibliographic record
Abstract
When sketching, we must choose between paper (expressive ease, ruler and eraser) and computational assistance (parametric support, a digital record). PHysically Assisted SKetching provides both, with a pen that displays force constraints with which the sketcher interacts as they draw on paper. Phasking provides passive, "bound" constraints (like a ruler); or actively "brings" the sketcher along a commanded path (e.g., a curve), which they can violate for creative variation. The sketcher modulates constraint strength (control sharing) by bearing down on the pen-tip. Phasking requires untethered, graded force-feedback, achieved by modifying a ballpoint drive that generates force through rolling surface contact. To understand phasking's viability, we implemented its interaction concepts, related them to sketching tasks and measured device performance. We assessed the experience of 10 sketchers, who could understand, use and delight in phasking, and who valued its control-sharing and digital twinning for productivity, creative control and learning to draw.
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.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".