[D69] End-user vibration customization tools: Parameters and examples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary form only given, as follows. Touch feedback (e.g., vibrations) can add to the expressiveness and utility of electronic devices, but users have a broad range of preferences as to their content and deployment. Rather than requiring of designers the nearly impossible task of pleasing everyone, we aim to empower users with easy-to-use tools that balance control with effort-of-use, for a desired degree of customizability. We focus in particular on affective qualities. In this demo, in the context of several application scenarios, we propose five parameters that can describe vibration customization tools, and demonstrate them with three tool concepts. Respectively, these follow themes of Choice (fast and convenient: choose individual stimuli), Filter (moderate control: modify base parameters of individual stimuli) and Block (high control: compose stimuli by arranging their component parts). Our aim is to open a discussion on end-user customization and tools, and learn of more contexts that could benefit from such an approach.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it