Integration: the key to implementing the Sustainable Development Goals
Bibliographic record
Abstract
On 25 September, 2015, world leaders met at the United Nations in New York, where they adopted the Sustainable Development Goals. These 17 goals and 169 targets set out an agenda for sustainable development for all nations that embraces economic growth, social inclusion, and environmental protection. Now, the agenda moves from agreeing the goals to implementing and ultimately achieving them. Across the goals, 42 targets focus on means of implementation, and the final goal, Goal 17, is entirely devoted to means of implementation. However, these implementation targets are largely silent about interlinkages and interdependencies among goals. This leaves open the possibility of perverse outcomes and unrealised synergies. We demonstrate that there must be greater attention on interlinkages in three areas: across sectors (e.g., finance, agriculture, energy, and transport), across societal actors (local authorities, government agencies, private sector, and civil society), and between and among low, medium and high income countries . Drawing on a global sustainability science and practice perspective, we provide seven recommendations to improve these interlinkages at both global and national levels, in relation to the UN’s categories of means of implementation: finance, technology, capacity building, trade, policy coherence, partnerships, and, finally, data, monitoring and accountability.
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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 | 0.079 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.027 | 0.035 |
| Open science | 0.004 | 0.054 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".