Developing benchmark criteria for assessing community-based social marketing programs
Bibliographic record
Abstract
Purpose – This article aims to propose that increased guidance on the implementation of social marketing principles for sustainability issues can advance both implementation and empirical evaluation. The primary goal of this paper is to ignite further empirical investigation of social marketing for sustainability by first presenting benchmark criteria for one social marketing model – community-based social marketing (CBSM) – and second, applying this framework to the case study of musician Jack Johnson’s “All at Once” (AAO) campaign. Design/methodology/approach – The research design is twofold. First, based on Doug McKenzie-Mohr’s CBSM model, a series of 21 benchmarks for assessing the key components of an effective CBSM initiative was developed. Second, this tool was applied to information gathered from Jack Johnson’s extensive outreach promoting AAO initiatives including reports, videos as well as interviews and in-person meetings with the Jack Johnson team. Findings – Application of the benchmark criteria to the Jack Johnson case study showed that seven out of the 21 benchmarks were integrated into the AAO campaign; seven were partially integrated and seven were not integrated in the program’s design. In particular, the use of commitments, incentives, norms and social diffusion was clearly present as was a final evaluation of the full-scale implementation of the campaign. Originality/value – The CBSM benchmarks are meant as a starting point to further assess and compare the effectiveness of CBSM initiatives. Further research should be done to explore how criteria should be weighted and which of the 21 principles need to be present in the design and implementation of an effective CBSM program.
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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 | 0.053 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".