Master’s thesis research in social marketing (1971-2015)
Bibliographic record
Abstract
Purpose Limited attention has been given to the study of social marketing at the graduate level. Such a study not only reveals research interests and trends, but also provides insights into the level of academic evolution or maturity of the social marketing field. This paper aims to examine social marketing as the subject of master’s theses. Design/methodology/approach A search strategy found 266 social marketing-focused master’s theses completed from 1971 to 2015. These theses were analysed by host countries, institutions, disciplinary contexts and degree programmes for which they were submitted. Findings Only four theses were submitted from 1971-1980 and eight completed in 1981-1990. The number of theses increased to 35 in 1991-2000, 118 between 2001 and 2010 and 101 in the past five years (2011-2015). The USA was the leading producer of social marketing master’s theses, followed by Canada, Sweden, China, South Africa, the UK and Kenya. A majority of theses were housed in the disciplines of business, health and communication, and none of them was submitted for a Master of Social Marketing degree. Originality/value This is the first study that investigates master’s theses with an exclusive focus on social marketing. Implications for the evolution, learning and teaching of social marketing are provided.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".