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 o ice, 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crisis, is guided by timely and trusted evidence.A world where research is not locked behind paywalls or delayed by outdated systems but delivered in real time and adapted to local needs.The world in 2025 faces complex challenges, but also unprecedented opportunities to accelerate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.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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.009 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".