Bibliographic record
Abstract
Background: The present paper focuses on compulsive buying, outlining the need to tackle this phenomenon using a social marketing approach, for the wellbeing of the affected individuals, their families and contacts, and for the health of our society at large. Focus of the Article: This conceptual development article is centered on behavior change and social marketing strategies that can address compulsive buying. Research Questions: How can social marketers help in curbing compulsive buying? What conceptual components and practical guidelines can be used in marketing programs for addressing compulsive shopping? Program Design/Approach: The platform developed herein outlines segmentation, targeting, product, price, place and promotional strategies recommended based on theoretical elements across disciplines. Importance to the Social Marketing Field: To date, compulsive buying has largely been ignored in the social marketing field, despite its relevance and prevalence. This paper provides a framework that can be employed in developing social marketing programs. Method: The proposed platform was created by bridging the literatures on compulsive buying and social marketing, identifying useful theoretical elements (e.g., the potential of the Thranstheoretical model), adapting and customizing these elements to provide actionable insights for intervention programs. The toolkit used for tackling other addictions was taken into account and integrated into the current development. Future Research: This paper offers an initial framework for social marketing efforts aimed at compulsive buying. It hopes to inspire significantly more work in this area to explore the potential of other theories and approaches to foster behavioral change for the better.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".