Expanding the Information Fidelity of Calm Technology Devices Through Techniques of Information Visualization
Bibliographic record
Abstract
This thesis explores the creation of ambient data visualisation devices that keep users informed via their use of colour and light to convey information. It is an exploration into how the principles of information visualisation can be applied to the design of “calm” devices in order to enhance their features as well as expand their information fidelity. This exploration builds on the concept of “calm technology”, coined by Mark Weiser and John Seely Brown of XEROX PARC (1996) which describes unobtrusive, informative technology. Calm technologies should exist mostly in the periphery, continuously relaying information in a non- intrusive manner. The research employs iterative prototyping and reflection to explore was to improve information delivery within calm technology. The project demonstrates several opportunities to enhance visual-based calm technology devices by incorporating principles of information visualisation in order to expand the information fidelity of these devices.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".