More than technology: Experiences of Virtual Emergency Operations Centers (VEOCs) during the COVID-19 pandemic response in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic has necessitated emergency management offices and organizations across Canada to activate their Emergency Operations Center (EOC) in a virtual capacity due to government restrictions limiting in-person activities and with the goal of reducing the spread of the virus. The aim of this exploratory research paper is to document the personal experiences of Canadian emergency management professionals working in a Virtual EOC (VEOC) environment during the COVID-19 response, including challenges and benefits they experienced, as well as lessons identified. Based on a sample of 81 emergency management professionals and using an inductive coding approach, the survey results illustrate both technological and nontechnological challenges and benefits. The findings highlight the need to incorporate three main elements into VEOC planning and operations: technology, processes, and people.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it