Sense-making in a Simulated Emergency Operations Centre
Bibliographic record
Abstract
This study reports the results of a laboratory experiment in decision-making in an Emergency Operations Centre (EOC) during a crisis. It adapted a validated simulation of a severe winter storm in a fictional city in Canada, during which a variety of serious events caused major disruptions, including loss of life. Participants were naïve individuals, as the focus was on sense-making in unfamiliar dynamic and uncertain environments and situations requiring urgent responses (i.e., conditions that occur during crises). The objective was to assess the impacts of enactment on the retention of memory of recent experiences. The enactment was theorized as the basis for sense-making processes where taking an action is guided by learning through acting. In contrast, predictions based on a simple model, the Zeigarnik effect, indicate the depletion of memory for completed actions, thus indicating their perverse effects on the capacity to learn. This experiment showed that participants in our simulation who played the role of passive observers of EOC activities during a severe storm remembered more of the events than did participants who played the role of trainees in the EOC who had to act on requests for information and decisions. Other results pointed to the conclusion that the role played by participants in the EOC significantly affected which events they remembered, their mood, and their priorities in the EOC. These results imply that having non-acting observers present in the EOC might be a way to better preserve organizational memory.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".