WaterHCI: Water in Human-Computer Interaction
Bibliographic record
Abstract
Over recent years, there has been an increase in the coming together of interactive technology and water, leading to the emergence of WaterHCI, a distinct subfield of humancomputer interaction (HCI). However, there is little work that aims to paint a comprehensive picture of the work around WaterHCI experiences so far, limiting the opportunity to identify directions for future research. This monograph aims to address this through an articulation of prior WaterHCI works structured using two frameworks that aim to offer a better understanding of the design of aquatic experiences through four key user experiences across six different degrees of contact with water. This articulation allows us to highlight underexplored areas that could guide WaterHCI researchers in identifying what to research next in order to bring the field forward as a whole. Ultimately, our work aims to help so that more people can profit from the many benefits that combining interactive technology and water affords.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".