The Influences of Extreme Cold and Storms on Schoolchildren
Bibliographic record
Abstract
The influences of extreme cold, rainfall, snowfall and wind on schools has received much less research than heat influences, yet there are findings starting to emerge on some of the impacts internationally. The impact of snow in some jurisdictions such as the USA, Canada and Finland can cause school closures and force students to miss regular days of school and set them behind in work requirements. For example, in the USA, school closures are 20 times more likely to occur in winter compared to summer, due to snowfall. Natural wind and rain disasters (such as flooding) from hurricanes and other major storms can also cause a surge in school closures and reduced attendance for students across the world. Research also shows that wet weather can cause issues in schools with reduced enjoyment, physical-activity participation and indoor spaces to occupy students, and wetter weather can be stressful for teachers to manage activity “backup plans.” This chapter will outline the range of cold, rainfall and windy weather extremes that can impact on schools internationally and will raise consideration of new strategies to ensure learning and physical activities from extreme weather interruptions can be prepared for, optimised and rebooted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".