Bibliographic record
Abstract
Ninety-seven percent of respondents to a recent poll either strongly agreed or agreed that serious injuries can be prevented by wearing a helmet during activities such as skateboarding, cycling, in-line skating and using scooters, and 95% strongly agreed or agreed that it is very important to wear a helmet at all times during these activities. However, adult Canadians' actual helmet use does not reflect these responses: only 35% of those who ride bicycles always wear a helmet while cycling, while 45% never do. Cyclists in British Columbia and the Atlantic provinces are most likely to wear helmets (61% and 56%), while those in Saskatchewan and Manitoba, and in Quebec, are least likely (12% and 25%). Parents' use of helmets appears to affect children's use. Among parents who always wear a helmet when bicycling, 98% say their children always wear one. Seventy-five percent of parents who report that they never wear a helmet when biking stated that their children also never wear a helmet. Findings were similar for parents and children who in-line skate, skateboard and ride scooters. Eighty-eight percent of respondents thought that public information and awareness campaigns would be very or somewhat effective in increasing the use of sports helmets. Eighty-one percent viewed safety courses and events as very or somewhat effective, and 77% thought government regulation would be very or somewhat effective in increasing helmet usage. The Ipsos-Reid poll involved interviews with 1000 Canadian adults. Results are considered accurate within ±3.1%, 19 times out of 20. — Shelley Martin, Senior Analyst, Research, Policy and Planning Directorate, CMA
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".