Using previous social marketing efforts to assess a new program
Bibliographic record
Abstract
Purpose This paper aims to report a research approach that explores how to use evaluations of previous social marketing efforts to assess and guide a new shelterbelt program called Working Tree. By targeting farmers, this new program aims to gain benefits from enhancing and expanding on-farm tree shelterbelts on the Canadian prairies. Design/methodology/approach This paper uses a novel method that relies on secondary data from six completed social marketing cases as data for a comparative analysis with the new program. A conceptual framework is proposed and applied. This framework incorporates process and outcome indicators of evaluation, key dimensions of the rational choice theory and proven practices from experience. Findings Analysis suggests key parameters of the Working Tree program to be appropriate, with some modifications. However, limitations in the data also point to avenues for future research to deepen the authors’ understanding of assessing a new social marketing program in the prelaunch phase. More research is needed on what works, where and why. Research limitations/implications The seven indices are a modest set for comparatives and are not exhaustive. Six selected cases are small samples that are unable to fully reflect the environmental nature of the new program; yet, they contained critical data for the comparative analysis. Financial data are not in constant dollars, which would be needed when further analysis is undertaken. Practical implications This paper illustrates the importance of the evaluation stage of the social marketing process. It demonstrates the practicality of being able to effectively draw upon previous evaluations to inform new program investors and social marketers at the prelaunch stage. Originality/value The conceptual framework and method present a novel approach to use evaluation data to guide new program funding and initiatives. It is offered with the hope that others might draw upon the ideas presented here and advance them.
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.
How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".