Macro‐social marketing and social engineering: a systems approach
Bibliographic record
Abstract
Purpose The purpose of this paper is to show how macro‐social marketing and social engineering can be integrated and to illustrate their use by governments as part of a positive social engineering intervention with examples from the Canadian anti‐smoking campaign. Design/methodology/approach This is a conceptual paper that uses the case of the Canadian anti‐smoking campaign to show that macro‐social marketing, as part of a wider systems approach, is a positive social engineering intervention. Findings The use of macro‐social marketing by governments is most effective when it is coupled with other interventions such as regulations, legislation, taxation, community mobilization, research, funding and education. When a government takes a systems approach to societal change, such as with the Canadian anti‐smoking campaign, this is positive use of social engineering. Research limitations/implications The social marketer can understand their role within the system and appreciate that they are potentially part of precipitating circumstances that make society susceptible to change. Social marketers further have a role in creating societal motivation to change, as well as promoting social flexibility, creating desirable images of change, attitudinal change and developing individual's skills, which contribute to macro‐level change. Practical implications Social marketers need to understand the structural and environmental factors contributing to the problem behavior and focus on the implementers and controllers of society‐wide strategic interventions. Social implications Eliminating all factors which enable problem behaviors creates an environmental context where it is easy for consumers to change behavior and maintain that change. Originality/value The value of this paper is in extending the literature on macro‐social marketing by governments and identifying the broader strategy they may be undertaking using positive social engineering. It is also in showing how marketers may use this information.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".