Crisis Crowdsourcing in Government: Characterising efforts by North American Agencies to Inform Emergency Management Operations
Bibliographic record
Abstract
Crowdsourcing is proven to be a useful communication platform during and in the direct aftermath of a disastrous event. While previous research in crisis crowdsourcing demonstrates its wide adoption for aiding response efforts, this research is generally limited to adoption by non-government organizations and members of the general public, and not government agencies. There is a gap in understanding the state of crowdsourcing by governments for emergency management. Additionally, there is a noticeable focus on the application of crowdsourcing in the response and recovery of a given disaster, with less attention paid to mitigation and preparedness. This research aims to classify the use of government crisis crowdsourcing in all phases of the disaster management cycle in Canada and the USA and identify the barriers and constraints faced by Canadian government agencies when adopting crisis crowdsourcing and social media for emergency management. Semi-structured interviews conducted with 22 government officials from Canada and the USA at the various levels of government in both countries reveal that crisis crowdsourced information has a place in all phases of the disaster management cycle, though direct crowdsourcing has yet to be applied in the pre-disaster phases. Participating federal agencies appear to be using crowdsourced information for mitigation and preparedness efforts, while the lower-tiered agencies are using crowdsourcing for direct response and recovery. A more in-depth analysis into the barriers and constraints faced by participating Canadian agencies looking to adopt crisis crowdsourcing or social media for emergency management reveals three general areas of concern that may be hindering crisis crowdsourcing efforts in Canada: organizational factors, demographic factors, and hazard risk. Based on these three general areas of concern, a readiness assessment scheme is presented to allow agencies to pinpoint the most prevalent barriers to their crowdsourcing efforts and to formulate plans to address these barriers.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".