How does tobacco growing impact advancement in demand reduction measures?
Bibliographic record
Abstract
Tobacco consumption remains a dire global health issue and the tobacco industry continues to find nefarious ways of keeping demand and consumption high.In the background lies the challenging, and often neglected, issue of tobacco growing and supply.Tobacco growing has proven to be a problematic practice due to its detrimental environmental impact 1 , the deeply troubling health implications of growing and processing 2 , and the precarious economic livelihoods of most farming households [3][4][5] .What requires particular attention is the critical relationship between tobacco supply (e.g.growing, processing, and manufacturing) and demand reduction measures.Here, we provide a glimpse into the ways that tobacco growing affects the ability of governments to institute demand reduction measures in tobacco growing countries.Many governments and other stakeholders in tobacco growing countries believe that commercial tobacco cultivation, particularly among small family-run farms, presents a path to macro-and micro-economic prosperity.On a macrolevel, governments particularly value the export and foreign exchange potential and the perceived contribution to employment.Tobacco can generate significant exports and foreign exchange, but the reward calculus tends to be superficial, overlooking the costs to farming households and the environment.Tobacco cultivation does employ thousands of farmers in some countries, but governments (and especially industry) typically oversimplify the nature of this employment.First, in most countries where researchers have rigorously measured livelihoods, many -often most -farmers consistently lose money cultivating tobacco [4][5][6][7][8] .Second, tobacco is typically only part, and sometimes a small part, of a farmer's crop portfolio and research demonstrates that many other crops and/or off-farm economic activities generate greater household resources 9 .The labour demands of tobacco are also great, with widespread use of child labor 10 .Third, tobacco farming causes Green Tobacco Sickness, a form of acute nicotine poisoning, among many who handle the leaf 11 .Fourth, the chemical overuse and deforestation from growing and curing tobacco devastate ecosystems and thereby the long-term health and economic prosperity of entire communities 6,12 .Put simply, if farmers reallocated their labor, land and/ or precious capital to other activities, it is likely that overall employment would increase and farmers' livelihoods and communities would improve.The misguided beliefs about the economic potential of tobacco growing have prompted governments to enact policies that encourage tobacco growing.By encouraging greater production, these policies typically drive down the costs and prices of tobacco thereby increasing consumption and motivating powerful actors to undermine tobacco control efforts.For example, in the latest Seventh National Development Plan, 2017-21, Zambia's government included tobacco growing as a priority crop, stating that tobacco 'has great potential to contribute
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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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".