Envisioning E3 Futures at the Seismic Science-Society Interface: Preliminary Analysis of the Pan Canadian Bilingual E3 Survey.
Bibliographic record
Abstract
The theme of the University of Calgary Natural Resources Canada (NRCan) funded project is: Addressing misconceptions around magnitude and intensity to inform earthquake early warning (EEW) alerting strategiesLast year, the Earthquake Early Warning Education (E3) socio cultural research project began. The E3 project has a dual purpose: to promote debate on the issues shaping Canadian earthquake risk management; and, to reflect on the biases in the media that reports on earthquakes. As seismic science - societal risk educators, we represent scholarship from across the spectrum (natural sciences, social sciences, and humanities). The E3 socio cultural project allows us to look ahead. We consider the contemporary and historical processes that shape how Canada will do E3 once the NRCan EEW is implemented in 2024. This presentation provides a preliminary report on the Pan Canadian bilingual E3 Survey results. Survey participants responses were needed to reveal the challenges this project faces. As a result, we have been provided with insights into the Canadian seismic science-society interface; and, the challenges this project faces. The E3 survey results will continue to make an impact both in Canadian E3 and in the training of future disaster risk management (DRM) practitioners. The E3 socio cultural project is an opportunity to talk about how Canada might use existing tools, perspectives, social media, and DRM training sessions to effectively support the NRCan EEW in 2024 and beyond.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".