Call to action: building a better future together, powered by evidence, guided by collective impact
Bibliographic record
Abstract
A better future starts with better evidenceImagine a world where every decision, whether in a government office, a community meeting, a hospital, or in response to a humanitarian crisis, is guided by timely and trusted evidence.A world where research is not locked behind paywalls or delayed by outdated systems but delivered in real time and adapted to local needs.The world in 2025 faces complex challenges, but also unprecedented opportunities to accelerate progress.The Sustainable Development Goals remind us how far we still need to go, while also highlighting the transformative power of working together in new ways [1][2].Guided by the principle of collective impact, and powered by new tools, global collaboration, and a pressing need for smarter, fairer decisions, we can reimagine how evidence drives progress.Across health, food systems, education, disaster preparedness, social protection, environmental protection and climate resilience, a stronger global evidence synthesis ecosystem can close the gap between knowledge and action.This is the future the Evidence Synthesis Infrastructure Collaborative (ESIC) is striving to build: timely, inclusive, and reliable evidence, created through robust, interoperable systems, which accelerates development goals and improves lives everywhere.This call to action invites governments, funders, evidence producers, intermediaries and citizens to shape that future together, replacing fragmentation with shared infrastructure, transforming how evidence is produced and used, and ensuring it reaches those who need it most -quickly and equitably.Let's act now.Let's choose a future where evidence drives collective impact.Evidence synthesis has expanded in scope and scale, but the infrastructure has not kept pace with contemporary needs.• Too o en, evidence synthesis is driven by academic incentives rather than user demand, produced in formats inaccessible to policymakers and other decision makers, or completed too late to inform urgent decisions [3].• The COVID-19 pandemic showed both the potential and fragility of the system [4].Extraordinary collaborations delivered timely evidence syntheses in some areas, yet inequities in funding, leadership, and access persisted, leaving many regions dependent on institutions in the Global North [5].• Geographic imbalances remain stark.Infrastructure for evidence synthesis is concentrated in a handful of high-income countries.Low-and middle-income countries are home to most of the world's population and rich in contextual knowledge, but they lack sustained support [6].Over-reliance on Global North institutions limits global capacity and undermines resilience in crises that demand context-specific solutions.• Most of today's evidence-synthesis infrastructure is fragile and sustained by volunteers.Even when vital assets are built and widely used, they o en struggle to secure long-term funding.• Fragmentation and duplication are widespread.Evidence is o en generated in silos across disciplines and sectors, with little interoperability or reuse.Artificial intelligence (AI) tools are emerging, with the potential to improve efficiency.But, without responsible strategies that leverage the empirically based methods of evidence synthesis, they risk exacerbating problems of transparency, accuracy, and trust.Unless addressed, these factors will continue to limit the impact of evidence synthesis.With deliberate investment, equitable collaboration, unbiased evidence and responsible innovation, we can build an infrastructure that is truly global, responsive, and fit for the future. Five steps to transformation in five yearsSupported by the Wellcome Trust, ESIC's open planning process engaged over 200 individuals from diverse sectors, regions, and disciplines.Over six months, they co-created a Roadmap for transformation [7], which was stress-tested at the 'Cape Town Consensus' meeting in June 2025 [8].The ESIC roadmap sets out a practical, scalable framework, with five steps to tackle systemic challenges, grounded in collective impact, collaboration, and equity (Figure Figure 1).Call to action: building a better future together, powered by evidence, guided by collective impact (Editorial) 1
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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 | 0.142 | 0.198 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.012 | 0.042 |
| Scholarly communication | 0.048 | 0.070 |
| Open science | 0.009 | 0.054 |
| Research integrity | 0.048 | 0.058 |
| Insufficient payload (model declined to judge) | 0.043 | 0.022 |
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".