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
Abstract: The bulk of applications for haptic feedback employ direct rendering approaches wherein a user touches a virtual model of some “real ” thing, often displayed graphically as well. We propose a new class of applications based on abstract messages, ranging from “haptic icons ” – brief signals conveying an object’s or event’s state, function or content – to an expressive haptic language for interpersonal communication. Building this language requires us to understand how synthetic haptic signals are perceived, and what they can mean to us. Experiments presented here address the perception question by using an efficient version of Multidimensional Scaling (MDS) to extract perceptual axes for complex haptic icons: once this space is mapped, icons can be designed to maximize both differentiability and individual salience. Results show that a set of icons constructed by varying the frequency, magnitude and shape of 2-sec, time-invariant wave shapes map to two perceptual axes, which differ depending on the signals ’ frequency range; and suggest that expressive capability is maximized in one frequency subspace.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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