Bibliographic record
Abstract
This report describes the results of a regularly distributed survey of nationwide Emergency Managers (EMs) as part of The Extreme Weather and Emergency Management Survey (WxEM) series. This project aims to send surveys to EMs across the United States three to four times a year, although that frequency may change based on EM and research needs. The Extreme Weather and Emergency Management Survey, Wave 7 (WxEM Wave 7) was designed by the Institute for Public Policy Research and Analysis (IPPRA) at the University of Oklahoma and collaborators from the Global System Laboratory and the Cooperative Institute for Research in the Atmosphere. It is the seventh survey in the series. WxEM Wave 7 opened on February 19, 2025, using an online questionnaire and has been completed by 292 EM personnel that were recruited from an IPPRA built database of EMs from across the country (see Wanless et al. 2023a for more info on recruitment). WxEM Wave 7 follows surveys that covered a variety of topics, working to better understand Emergency Management operations, especially in the weather space. Wave 7 investigated Emergency Manager operations during wildfire events, especially their reception, use, and sharing of fire weather forecast information. This report presents an overview of the methodology of the survey data collection, and a reproduction of the survey instrument with frequencies for the questions that elicited numeric responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 0.119 |
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".