‘Drop, cover and hold on’ or ‘triangle of life’ attributes of information sources influencing earthquake protective actions
Bibliographic record
Abstract
A well-known fact is that an earthquake or earth shaking does not cause injuries and deaths.rather, buildings and infrastructure systems collapsing on people do.Hence, reputable government organizations from countries prone to high earthquake risks are heavily invested in advising their populations on immediate lifesaving protective actions (PAs).One such action is the 'Drop, Cover and Hold on' strategy proven to have saved countless lives.unfortunately, in recent years another action known as the 'Triangle of life' has been trolled through internet sites and hearsay.It is believed that adopting such an unsubstantiated erroneous action is likely to put people at greater risk during an earthquake.Thus, there is a need to extend studies to understand factors that influence people's decisions to take certain PAs over another for earthquakes.This research does that through an empirical study of 647 residents from mianyang City in the Sichuan province of China.The results indicate that if a PA is easy to understand, mentioned often by multiple sources and easy to access, then people will adopt it.but a striking finding is that people are also likely to be influenced by wrong information, depending on who is providing such information and through which medium (e.g.social media).These findings suggest that the Chinese government needs to provide gate keepers who are dedicated, trained personnel who can monitor misinformation on various Internet sites and address them.In parallel they can provide regular, up to date public advisories on immediate PA through multiple legitimate government, private and non-profit sector sources and channels.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".