Communication under Fire: The Role of Embeddedness in the Emergence and Efficacy of Disaster Response Communication Networks
Bibliographic record
Abstract
Communication networks among responders are critical to effective coordination and information transfer across agencies active in a disaster response. Using the theory of embeddedness, we investigate how aspects of relational and institutional embeddedness influence the emergence and efficacy of interactions among responding agencies using network data from three significant wildfire events in the wildland/urban interface. For this study, relational embeddedness is investigated as the degree of familiarity between two responders before the incident. Institutional embeddedness is explored in terms of nesting within shared affiliations and common roles. Our findings suggest that both relational and institutional embeddedness significantly shape the disaster communication network during an incident, but relational embeddedness appears to play a stronger role. Further, the most problematic interactions appear to occur among institutionally embedded responders who do not know each other. Consequently, knowing something about relational and institutional embeddedness within the network of responders before an incident provides insight into what the communication network will look like when a disaster occurs. Findings also provide insights for how we might mitigate risk for problematic information flow and coordination during the incident.
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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 | 0.004 | 0.028 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 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 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".