New ideas - fresh thinking: towards a broadening of the social marketing concept? (Editorial)
Bibliographic record
Abstract
Recent commentaries (Donovan, 2011; Gordon, 2012; Spotswood et al., 2012), and discussion at the World Social Marketing Conference in Toronto in April 2013 have highlighted that there are a number of debates around the fundamentals of social marketing. For a number of years, scholars have been calling for social marketing to mature (Andreasen, 2003), spread its wings and develop its own theories (Peattie and Peattie, 2003). Furthermore, there is recognition that social marketing needs to demonstrate its legitimacy not only as an approach to social change, but as an academic discipline. Recently, these debates have intensified as those in the field discuss the merits or otherwise of singular definitions of social marketing, and the appropriate social marketing mix. Discussions on the utility of wholesale transference of marketing theory and concepts, services orientation, and value creation have also emerged. In addition, ideas on critical thinking and reflexivity in social marketing are increasingly prominent. These developments are concomitant with complex societal challenges. These include the global financial crisis, environmental sustainability, corporate malevolence, increasing economic, health, social, and cultural inequalities, ageing populations, migration and immigration issues, and the erosion of social welfare programmes.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.023 | 0.031 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".