The COVID-19 pandemic has thrown the SDGs into reverse: The financial sector can play its part in the recovery by excluding tobacco
Bibliographic record
Abstract
The United Nations has warned that the COVID-19 pandemic threatens to reverse decades of progress on poverty reduction, inequality, hunger, health, and education.It has described the pandemic as the worst human and economic crisis of our lifetime with the result that the UN's 17 Sustainable Development Goals (SDGs) are now even further out of reach 1 .It has called for 'political leadership, solidarity and unity' to overcome the crisis, and has expressly called on the private sector and financial institutions to ensure that every investment decision they make supports an inclusive and sustainable recovery and takes into account Social, Environmental and Governance (ESG) factors 2 .While the focus is often on the tobacco control measures that governments should and must take, the financial sector must also play its part to support a new type of economy -one which protects both people and the planet, and advances the SDGs. How tobacco negatively impacts the SDGsTobacco production and consumption are incompatible with the achievement of the SDGs.Indeed, our work at Tobacco Free Portfolios shows that tobacco negatively impacts 14 of the 17 SDGs 3 .Tobacco kills an estimated 8 million people per year and is the single largest cause of preventable death worldwide, limiting progress on SDG 3 on health and wellbeing.It also kills or disables people in their prime productive years, negatively affecting SDG 8 on economic growth.In some low-income countries like Malawi and Zambia, tobacco -a crop which has no nutritional value -has displaced food crops, leading to increased food insecurity (affecting SDG 2 on zero hunger).In many of those same countries, children are forced to work in tobacco fields, limiting their educational opportunities and life chances (SDG 4 on education).We also see that tobacco consumption worsens inequalities both between and within countries since smoking is more prevalent in developing countries (where public health systems are less able to cope) as well as within disadvantaged communities in all countries (SDG 10 on reduced inequalities).Tobacco production also leads to biodiversity loss, land pollution, soil degradation and deforestation, all of which reduce progress towards SDG 13 on climate action and SDG 15 on life on land.Meanwhile, an estimated 4.5 trillion cigarette filters are discarded every year, many of which end up in oceans and beaches, negatively impacting SDG 14 (life under water).The list goes on.Tobacco is therefore simultaneously a public health issue, an equality issue, a human rights issue, an international development issue, an environmental issue and a climate change issue.The financial sector has meanwhile made laudable commitments to support
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".