Risk Communication and Public Education in Edmonton, Alberta, Canada on the 10th Anniversary of the ‘Black Friday’ Tornado
Bibliographic record
Abstract
In July 1997, on the 10th anniversary of the great ‘Black Friday’ Tornado, city officials of Edmonton, the print and broadcast media, agencies dealing in emergency management, and the national weather organisation recounted stories of the 1987, F5 tornado that struck Edmonton on a holiday weekend. The information campaign also presented environmental and educational information regarding a range of protective measures that should be adopted in the event of another tornado strike. A unique opportunity arose to study the effects of the 1997 risk communication campaign, and to assess the extent to which a random sample from the population of Edmonton heard, understood, believed, confirmed, and responded to the low-key, non-urgent, environmental and educational warning messages. These behaviours comprise the General Hazards Risk Communication Model that guided this study, as developed by Mileti, Sorensen, Haas, Blanchard-Boehm and others. We found the following explanatory variables to be statistically significant in predicting whether our survey respondents adopted protective measures towards future occurrences of tornadoes following the information campaign: (1) levels of perceived vulnerability to future occurrences; (2) past experiences with the 1987 tornado event; (3) presentation of new environmental and educational information in the 1997 campaign; and, (4) levels of formal education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".