Indigenous peoples, tobacco use and the role of the commercial tobacco industry
Bibliographic record
Abstract
Advertising has appropriated and used Indigenous imagery targeted toward Indigenous peoples. In the United States, R.J. Reynolds markets and profits from selling Natural American Spirit (Figure 1A ), which uses Indigenous imagery and misleading packaging, creating false perceptions that ‘organic’ or ‘natural’ cigarettes are less harmful. In the 1980s, tobacco company WD & HO Wills ran advertising in Australia with the slogan ‘Get your own black’. In the 1990s, Winfield advertisements depicted an Aboriginal man playing a didgeridoo with the slogan ‘Australians' answer to the peace pipe’. More recently, Philip Morris International (PMI) sold cigarettes in Israel labelled ‘Māori Mix’ and contacted Aboriginal organizations to promote e‐cigarettes (Figure 1B ). Additionally, the Centre for Research Excellence: Indigenous Sovereignty and Smoking (COREISS) was established with funding from the PMI‐funded Foundation for a Smokefree World funded. COREISS has opposed public health initiatives, including the Smokefree Aotearoa 2025 Action Plan and restrictions on smoking in cars with children. The Director of COREISS addressed the New Zealand Health Select Committee regarding proposed legislation to ban smoking in cars with children in 2019, stating: L.J.W. was supported by an NHMRC Investigator Grant (grant number: 2009380).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".