Developing more effective social marketing strategies
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 The reason for this paper is to better understand why many social marketing campaigns produce poor results and to propose a model to guide social marketing strategic planning to improve program outcomes. Design/methodology/approach This is a conceptual paper which discusses a new social marketing model to remove upstream causes of target social problems. Findings It appears that social marketing planning may be limited by over‐reliance on commercial marketing tactics and an over‐emphasis on individual behavior change. Finding upstream sources of social problems is a first step. However, social marketers must be willing to employ tactics to ameliorate structural, upstream causes of social problems. Research limitations/implications The social marketing field needs to further its developmental progress by reducing its use of commercial marketing concepts and increasing its use of concepts from other fields like public health, political science, and social movements. Practical implications Practicing social marketers can improve their outcomes if they identify upstream causes of social problems and find ways to reduce their harmful effects. Social implications There are major social implications because removing upstream sources of social problems will invoke opposition from powerful interests. A new array of complexity is involved in using activism as a tactic, which may be needed. Conflicts will have to be dealt with and responded to effectively. Originality/value The value of this paper is to enhance awareness of the self‐imposed limitations on social marketing strategies and to propose a means of removing these limitations and improving the ability to improve social well‐being.
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 | 0.004 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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