Unraveling human drivers behind complex interrelationships among sustainable development goals: a demonstration in a flagship protected area
Bibliographic record
Abstract
The transformational potential of the United Nations’ 2030 Agenda for Sustainable Development Goals (SDGs) lies in effective efforts to reconcile the conflicts and maximize the synergies among the interrelated SDGs. Previous research on the interrelationships among SDGs often focused on depicting the degree to which different goals reinforce or hamper each other; however, drivers behind these interrelationships have rarely been evaluated. We developed a novel approach to unraveling the impact of human activities on the complex trade-offs and synergies among SDGs. We used the approach to assess the impacts of four globally common livelihoods, including cropping, local off-farm labor work, labor migration, and livestock husbandry, on the interrelationships among SDG 1 (no poverty), SDG 3 (enhance human well-being), and SDG 15 (protect life on land) in a demonstration site. The results show that our approach can be very useful in informing coherent governance and facilitating progress toward SDGs across social, economic, and environmental dimensions simultaneously.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".