Developing benchmark criteria for assessing community-based social marketing programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it