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.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".