Best practices in social marketing among Aboriginal people
Bibliographic record
Abstract
Purpose – The purpose of this paper is to identify, summarize and assess literature focused on developing social marketing programs for Aboriginal people. Design/methodology/approach – The authors conducted a literature search and review of research papers concerning social marketing and Aboriginal populations over the period 2003-2013. Findings – The research reveals very little published research (N = 16). The literature points to a wide range of findings including the importance of segmenting/targeting and avoiding pan-Aboriginal campaigns; cultural importance of family and community; the importance of multi-channels; universal value of mainstream and Aboriginal media outlets, use of print media, value of elders and story-telling for message dissemination; increasingly important role of Internet-based technology; need for campaign development to reflect Aboriginal culture; and importance of formative research to inform campaign development. Social implications – Considerable research is warranted to better develop more effective social marketing campaigns targeted to Aboriginal audiences to improve health outcomes for such groups across the globe. Originality/value – This paper provides a baseline foundation upon which future social marketing research can be built. It also acts as a call to action for future research and theory in this important field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".