Using Sonification to Explore Texting Response Time in Time Stamped Interactional Data
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
We examine the utility of sonification for exploringtemporal patterns in time stamped logs of textmessages. Using sonification, we identify patterns in asubset of the logs, and examine how these patternsvary by relational closeness. We then verify thesepatterns’ generalizability in the full dataset usingstatistical analysis.Nous examinons l’utilité de la sonification pourexplorer les tendances temporelles dans les journauxhorodatés de messages texte. Grâce à la sonification,nous identifions les motifs dans un sous-ensembledes journaux, et nous examinons comment ces motifsvarient selon la proximité relationnelle. Nous vérifionsalors si la généralisation de ces motifs est possible etextensible à l’ensemble des données en utilisant uneanalyse statistique.
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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 | 0.003 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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