Bibliographic record
Abstract
Purpose Although the social marketing field has developed relatively quickly, little is known about the careers of students who chose social marketing as their main subject of study. Such research is important not only because it reveals employment trends and mobility but also because it informs policy making with respect to curriculum development as well as raises governmental and societal interest in the social marketing field. This paper aims to analyse the career pathways of doctoral graduates who examined social marketing as the subject of their theses. Doctoral graduates represent a special group in a knowledge economy, who are considered the best qualified for the creation and dissemination of knowledge and innovation. Design/methodology/approach A search strategy identified 209 doctoral-level social marketing theses completed between 1971 and 2015. A survey was then delivered to dissertation authors, which received 117 valid responses. Findings Results indicate that upon graduation, most graduates secured full-time jobs, where about 66 per cent worked in higher education, whereas the others worked in the government, not-for-profit and private sectors. Currently, there is a slight decline in the number of graduates employed in the higher education, government and not-for-profit sectors but an increase in self-employed graduates. A majority of graduates are working in the USA, the UK, Australia and Canada. Overall, levels of international mobility and research collaboration are relatively low. Originality/value This is arguably the first study to examine the career paths of social marketing doctoral graduates.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".