Barriers to Use of Social Media by Emergency Managers
Bibliographic record
Abstract
Abstract Social media (SM) are socio-technical systems that have the potential to provide real-time information during crises and thus to help protect lives and property. Yet, US emergency management (EM) agencies do not extensively use them. This mixed-methods study describes the ways SM is used by county-level US emergency managers, barriers to effective SM use, and recommendations to improve use. Exploratory interviews were conducted with US public sector emergency managers to elicit attitudes about SM. This was followed by a survey of over 200 US county level emergency managers. Results show that only about half of agencies use SM at all. About one quarter of agencies with formal policies actually forbid the use of SM. For both disseminating (sending out) and collecting information lack of sufficient staff is the most important barrier. However, lack of guidance/policy documents is the second highest rated barrier to dissemination via SM. Lack of skills and of the training that could improve these skills is also important. For collecting data, trustworthiness and information overload issues are the second and third most important barriers, which points to the need for appropriate software support to deal with these system-related issues. There are few differences associated with agency characteristics. By understanding important barriers, technologists can better meet the needs of emergency managers when designing SM technologies.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".