Radon in Schools: A Review of Radon Testing Efforts in Canadian Schools
Bibliographic record
Abstract
Radon, a known carcinogen, becomes a health risk when it accumulates inside buildings. Exposure is of particular concern for children, as their longer life expectancy increases their lifetime risk of developing cancer. In 2016, 5.5 million students were enrolled in Canadian elementary and secondary schools. With no national policy on radon testing in schools, children may be at risk from radon exposure while attending school and school-based programs. This study explored radon testing efforts in publicly funded Canadian schools and summarizes where testing programs have occurred. Radon testing in schools was identified through a systematic qualitative enquiry, surveying members from different levels of government (health and education) and other stakeholders (school boards, research experts, among others). Overall, this research found that approaches to radon testing varied considerably by province and region. Responsibility for radon testing in schools was often deferred between government, school boards, building managers and construction parties. Transparency around radon testing, including which schools had been tested and whether radon levels had been mitigated, also emerged as an issue. Radon testing of schools across Canada, including mitigation and clear communication strategies, needs to improve to ensure a healthy indoor environment for staff and students.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".