"Snow days are the best days." Exploring Teachers' and Administrators’ Perceptions of Weather-Related School Disruptions
Bibliographic record
Abstract
Extreme weather events are becoming increasingly common and have the potential to impact the school day. This study aimed to explore teachers’ and administrators’ perspectives on weather-related school closures. Semi-structured interviews were conducted with ten key informants and analyzed using content analysis. Informants took a strengths-based approach and discussed the benefits of weather-related disruptions for student mental health and planning time. However, informants did mention that if these days continued to rise, it might be a cause for concern. School boards need to begin monitoring the impact of weather events. Les phénomènes météorologiques extrêmes sont de plus en plus fréquents et peuvent avoir une incidence sur la journée scolaire. Cette étude visait à explorer les perspectives des enseignants et des administrateurs sur les fermetures d'écoles liées aux conditions météorologiques. Des entrevues semi-structurées ont été menées avec dix informateurs clés et analysés à l'aide d'une analyse de contenu. Les informateurs ont adopté une approche fondée sur les points forts et ont discuté des avantages des perturbations liées aux conditions météorologiques pour la santé mentale des élèves et le temps de planification. Cependant, les informateurs ont mentionné que si ces jours continuaient à augmenter, cela pourrait être une source d'inquiétude. Les conseils scolaires doivent commencer à surveiller l'incidence des événements météorologiques.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".