Benefits and Limitations of the Social Network Analysis When Explaining Instances of Ineffective Communication in Two Chemical, Biological, Radiological, Nuclear, and Explosives Simulations
Bibliographic record
Abstract
Social Network Analysis (SNA) was performed on a number of emergency response scenarios, including here on two Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) simulations. However, little evidence exists in the literature pertaining to the explanation of communication breakdowns using SNA. In this paper, the SNAs of two CBRNE simulations were compared and the differences in structure were related to instances of ineffective communication. Study 1 had two tiers in the response (commanders and first responders) and Study 2 had three tiers, where Operations (Ops) officers were added between the commanders and first responders. A higher percentage of communication breakdowns were found in Study 2, possibly as a result of the additional layer. However, the two studies had different scenarios and CBRNE responders, both possibly confounding the findings. Researchers using SNA are provided with a convenient representation and summary of team functioning. However basic SNA does not help researchers to distinguish between effective communication and breakdown. Communication breakdowns were attributed to long multi-hop communications, which seldom occurred in the present studies because of the small number of participants in the network, and the large number of communications among them.
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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 | 0.030 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".