I can feel it moving: Science Communicators Talking About the Potential of Mid-Air Haptics
Bibliographic record
Abstract
We explored the potential of haptics for improving science communication, and recognized that mid-air haptic interaction supports public engagement with science in three relevant themes. While science instruction often focuses on the cognitive domain of acquiring new knowledge, in science communication the primary goal is to produce personal responses, such as awareness, enjoyment, or interest in science. Science communicators seek novel ways of communicating with the public, often using new technologies to produce personal responses. Thus, we explored how mid-air haptics technology could play a role in communicating scientific concepts. We prototyped six mid-air haptic probes for three thematic areas: particle physics, quantum mechanics, cell biology; and conducted three qualitative focus group sessions with domain expert science communicators. Participants highlighted values of the dynamic features of mid-air haptics, its ability to produce shared experiences, and its flexibility in communicating scientific concepts through metaphors and stories. We discuss how mid-air haptics can complement existing approaches of science communication, for example multimedia experiences or live exhibits by helping to create enjoyment or interest, generalized to any fields of science.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".