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
In this paper, we explore how people use touchscreens to express emotional intensity, and whether these intensities can be understood by oneself at a later date or by others. In a controlled study, 26 participants were asked to express a set of emotions mapped to predefined gestures, at range of different intensities. One week later, participants were asked to identify the emotional intensity visualized in animations of the gestures made by themselves and by other participants. Our participants expressed emotional intensity using gesture length, pressure, and speed primarily; the choice of attributes was impacted by the specific emotion, and the range and rate of increase of these attributes varied by individual and by emotion. Recognition accuracy of emotional intensity was higher at extreme ends, and was higher for one's own gestures than those made by others. The attributes of size and pressure (mapped to color in the animation) were most readily interpreted, while speed was more difficult to differentiate. We discuss human gesture drawing patterns to express emotional intensities and implications for developers of annotation systems and other touchscreen interfaces that wish to capture affect.
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) | 0.011 | 0.002 |
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