Policy coherence, health and the sustainable development goals: a health impact assessment of the Trans-Pacific Partnership
Bibliographic record
Abstract
The international community, comprised of national governments, multilateral agencies and civil society organisations, has recently negotiated a set of 17 sustainable development goals (SDGs) and 169 targets to replace the Millennium Development Goals, which expired in 2015. For progress in implementing the SDGs, ensuring policy coherence for sustainable development will be essential. We conducted a health impact assessment to identify potential incoherences between contemporary regional trade agreements (RTAs) and nutrition and health-related SDGs. Our findings suggest that obligations in RTAs may conflict with several of the SDGs. Areas of policy incoherence include the spread of unhealthy commodities, threats to equitable access to essential health services, medicines and vaccines, and reduced government regulatory flexibility. Scenarios for future incoherence are identified, with recommendations for how these can be avoided or mitigated. While recognising that governments have multiple policy objectives that may not always be coherent, we contend that states implementing the SDGs must give greater attention to ensure that binding trade agreements do not undermine the achievement of SDG targets.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".