Pan Canadian Narratives and Numbers: A Knowledge to Action Initiative in the Earthquake Early Warning Education (E3) Project
Bibliographic record
Abstract
<!--!introduction!--> Partnerships with Earth Science for Society, the Canadian Risk and Hazards Network, the Assembly of First Nations, the International Association of Emergency Managers, the Canadian Federation of Earth Sciences and others have been critical for reaching the target audience of the Pan Canadian bilingual E3 Survey. Research partners also circulated our 2022 article entitled A new earthquake warning system will prepare Canada for dangerous shaking and its key message: “About 10 million people live in Canada’s earthquake-prone zones. Yet few have practical knowledge of what to do with new early warning system alerts which aim to save lives and protect livelihoods” . Importantly, such partnerships also provide the structure for the distribution of the E3 Survey final report. The primary aim of this presentation is to outline how our partners have helped us to research and report on three questions of national interest: What connects Canadian science education, earthquake literacy and time/space specific skills required to succeed in earthquake prone locations ? How do Canadians build their earthquake literacy? Why does Canada needs earthquake literacy taught in disaster risk management activities and programs? We showcase how the Knowledge to Action Framework analysis of E3 Survey results reveals multiple aspects of E3 delivery that need to change. We suggest that with evidence based recommendations, our partners can identify resource needs and pinpoint future interventions. Overall, we conclude that investment in future E3 products – via digital delivery - may revolutionize E3 geoliteracy levels and close the E3 Knowledge to Action gap.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.039 | 0.010 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".