Multimedia-Enabled 911: Exploring 911 Callers’ Experience of Call Taker Controlled Video Calling in Simulated Emergencies
Bibliographic record
Abstract
Emergency response to large-scale disasters is often supported with multimedia from social media. However, while these features are common in everyday video calls, the complex needs of 911 and other systems make it difficult to directly incorporate these features. We assess an ME911 (Multimedia-Enabled 911) app to understand how the design will need to deviate from common norms and how callers will respond to those non-standard choices. We expand the role of 911 call taker control over emergency situations to the calling interface while incorporating key features like map-based location finding. Participants’ experiences in mock emergencies show the non-standard design helps callers in the unfamiliar setting of emergency calling yet it also causes confusion and delays. We find the need for emergency-specific deviations from design norms is supported by participant feedback. We discuss how broader system changes will support callers to use these non-standard designs during emergencies.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".