It's the Gesture That (re)Counts: Annotating While Running to Recall Affective Experience
Bibliographic record
Abstract
We present results from a study exploring whether gestural annotations of felt emotion presented on a map-based visualization can support recall of affective experience during recreational runs. We compare gestural annotations with audio and video notes and a “mental note” baseline. In our study, 20 runners were asked to record their emotional state at regular intervals while running a familiar route. Each runner used one of the four methods to capture emotion over four separate runs. Five days after the last run, runners used an interactive map-based visualization to review and recall their running experiences. Results indicate that gestural annotation promoted recall of affective experience more effectively than the baseline condition, as measured by confidence in recall and detail provided. Gestural annotation was also comparable to video and audio annotation in terms of recollection confidence and detail. Audio annotation supported recall primarily through the runner's spoken annotation, but sound in the background was sometimes used. Video annotation yielded the most detail, much directly related to visual cues in the video, however using video annotations required runners to stop during their runs. Given these results we propose that background logging of ambient sounds and video may supplement gestural annotation.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".