A historical case study teaching about earthquake intensity and magnitude: Research to help Canadians better understand earthquake early warning alerts
Bibliographic record
Abstract
<!--!introduction!--> Canada is developing an earthquake early warning system (EEWS) for implementation in 2024. To help prepare the Canadian public to take appropriate protective action when getting an EEW alert, educating them about the phenomenon of earthquakes is essential. To this end, we are developing a historical case study focusing on the conceptual development of earthquake intensity and magnitude. Historical case studies in science education have usually focused on helping learners understand the nature of science better, but they have also been effective in teaching scientific content. The case study begins with the human experiences of earthquakes and how they used myths to explain the observations. The story then picks up in the 18th century and documents many different earthquakes (Lisbon, Portugal, 1755; Naples, Italy, 1857; Mino-Owari, Japan, 1891; Assam, India, 1897; San Francisco, USA, 1906; and Alaska, USA, 1964) and the development of understandings of what earthquakes are, how they happen, and how the concepts of intensity and magnitude played a role in those understandings. The case study reveals the switch from mythic explanations to reasoned ones; that earthquakes are natural occurrences and therefore steps can be taken to mitigate death and destruction. The narrative distinguishes between an observational era and one of the instruments. After the development of the seismometer, there was a much more quantitative approach to seismology. A stark contrast between earthquake intensity (very concrete, yet subjective) and magnitude (abstract, yet calculated) became quite apparent. By focusing on this contrast, readers will develop a robust understanding of both conceptions.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.040 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".