Public Response on Social Media to a Social Marketing Campaign for Influencing Attitudes towards Boating Safety
Bibliographic record
Abstract
The purpose of this research paper is to assess the response on Facebook to a social marketing campaign for recreational boating safety. The campaign ran for the 2018 and 2019 boating seasons in British Columbia, Canada. Messages related to boating safety were delivered in multi-media formats, including ten Facebook posts. All public comments on the campaign Facebook page in response to the ads were included in the analysis. Comments were reviewed for tone and subject; those that related directly to the campaign or boating safety-related topics, such as alcohol use or enforcement, were labeled positive, negative or neutral in tone. Metrics such as likes and shares were also noted. The overall engagement rate (defined as engagements over people reached) was 4.1%. The posts were liked >7000 times and received 901 shares. A total of 219 comments were analysed. Almost half of the comments were positive (n = 106, 48.4%). Fifty comments were off-topic (22.8%), 45 were neutral (20.5%) and 18 were negative (8.2%). The majority of comments were positive, indicating that the campaign performed as planned and was generally well received by the people for whom it was intended. Comments illuminated prevailing attitudes towards risks, injuries and safety practices related to recreational boating. Positive comments valued safety as an aspect of having a pleasant experience, rather than a barrier. Negative comments were about perceiving reduced fun of boating, rather than objecting to the campaign itself. As a component of a multi-media social marketing strategy, Facebook can be a source of instant feedback from the campaign audience.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".