Developments in Canadian community-based impaired driving initiatives: MADD Canada's "Campaign 911"
Bibliographic record
Abstract
Various programs have been undertaken in Canada and the United States to encourage the public to report suspected impaired drivers to the police. The elements of these programs have varied, few programs were assessed, and the collected data were incomplete. In 2007, MADD Canada launched its national lCampaign 911r to encourage the public to report suspected impaired drivers. MADD Canada is the countryrs largest grassroots anti-impaired driving organization, giving its programs considerable reach. This paper reviews the pre-existing public mobilization programs, describes the key elements of MADD Canadars Campaign 911 and assesses its reported impact. The results of MADD Canadars Campaign 911 have been promising. The reported benefits include: increased public perception of the risk of apprehension; increased public calls to the police regarding suspected impaired drivers; and increased police vehicle interceptions, provincial licence suspensions, federal impaired driving charges, and police follow-up with the owners of reported vehicles that were not intercepted. The elements of Campaign 911 are consistent with the research on effective media, traffic safety and multi-component community campaigns. Similarly, the focus on increasing police interception and charge rates is consistent with deterrence theory. However, the specific features, intensity and duration of the individual campaigns vary, and data on these campaigns have not been collected on a consistent basis. Nevertheless, given the promising results to date, MADD Canadars Campaign 911 warrants a systematic review.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".