Using the Internet to Build Community Capacity for Healthy Public Policy
Bibliographic record
Abstract
An interactive Web site and e-mail campaign became the primary focus of a coalition's community mobilization strategy to advocate for changes to the local smoking bylaw in a large Canadian urban center. This article presents the findings of an Internet survey of 2,200 Internet mailing list recipients in which 26% (n=605) submitted responses. Findings from four focus groups of the survey respondents (n=28) are also reported. The survey found that a majority of the mailing list respondents (66.1%) contacted the city council during the campaign. Only 35.8% of respondents had contacted a city council member prior to this campaign. As a result of their participation in the Internet campaign, 50.6% stated that they were more likely to get involved in future civic issues. These findings were confirmed by focus groups that found increased capacity for political involvement on this issue as well as capacity for future social and political action.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".