Call to action: building a better future together, powered by evidence, guided by collective impact
Notice bibliographique
Résumé
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crisis, is guided by timely and trusted evidence.A world where research is not locked behind paywalls or delayed by outdated systems but delivered in real time and adapted to local needs.The world in 2025 faces complex challenges, but also unprecedented opportunities to accelerate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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,142 | 0,198 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,007 | 0,005 |
| Études des sciences et des technologies | 0,012 | 0,042 |
| Communication savante | 0,048 | 0,070 |
| Science ouverte | 0,009 | 0,054 |
| Intégrité de la recherche | 0,048 | 0,058 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,043 | 0,022 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».