Community-Based Social Marketing in Theory and Practice: Five Case Studies of Water Efficiency Programs in Canada
Bibliographic record
Abstract
Background: Community-based social marketing (CBSM) offers a pragmatic five-step approach to developing a program that fosters sustainable behaviour. However, how the CBSM theoretical framework has been implemented into practice remains largely under-evaluated. To help address this gap, Lynes et al. developed 21 benchmarks to assess CBSM programs. This research builds upon these benchmarks by using both the benchmarks and additional assessment criteria to assess five Canadian programs that have used CBSM principles. Focus: This paper is related to research and evaluation of community-based social marketing. Research Question: How has the CBSM theoretical framework been implemented in practice at the community level? Importance to the Social Marketing Field: By exploring how five Canadian programs have implemented CBSM, this paper enables practitioners to align their programs with CBSM principles more closely. It also contributes to the literature on CBSM effectiveness. Methods: Five qualitative case studies were assessed, each featuring a Canadian community program seeking to influence residential water efficiency behaviour. In order to systematically assess each program’s adherence to the CBSM theoretical framework, a CBSM benchmark assessment tool that proposes additional assessment criteria to Lynes et al.’s 21 benchmarks was developed. The assessment tool allowed for replicable benchmark assessments across multiple programs. Triangulation of data from both primary (survey and interview) and secondary (peer-reviewed literature, gray literature, and online reporting) data sources informed the assessment of each case study. Results: On average, over the five case studies, just over half of the 21 benchmark criteria were fully integrated into the programs, whereas just under a third were partially integrated, and approximately one fifth were not integrated at all. Recommendations for Research or Practice: While the benchmarks were fairly well integrated overall, this paper outlines several recommendations that programs may consider to improve alignment with the CBSM theoretical framework and benchmarks. Recommendations for future research to explore CBSM effectiveness are also made. Limitations: Lack of generalizability due to small sample size, unable to make assessments of programmatic success, and inherent limitations of the benchmark assessment tool.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".