If you are working in this area or are interested in this topic, do get in touch!

The project has a comparative focus exploring: 1.) Settings where practices have been ‘augmented’ by introducing AI tools to enhance some elements of the review process; 2.) Settings where practices are being ‘replaced’ by the automated evidence infrastructure for real-time evidence synthesis.

The project will explore three levels of change brought about by AI: epistemic (how it reshapes knowledge production); organisational (how it changes professional norms and career trajectories); institutional (how it transforms science-policy interactions)

Thanks for contributing! It was fantastic!

Sounds great - I look forward to read your findings!

Thanks Anna!

Evidence cultures act as filters, making some forms of evidence visible and available while others are ignored and irrelevant. They shape epistemic repertoires of expertise, acting like interpretative frames of available evidence.

As with any culture, evidence culture is 'sticky' and difficult to change. However, it happens from time to time! I termed this process 'paradigmatic learning' as it requires not only the accumulation of knowledge but also institutional political and social shifts legitimising the change.

It focuses on how evidence and expertise are culturally driven phenomena. Therefore, what 'counts' as robust evidence differs across organisations. Social processes reinforce these evidentiary standards, for example, networks of mentorship and collaboration or key norm entrepreneurs promoting them.

Congrats Jelena! Sounds really exciting!

This is the most genius idea I have ever heard!

Fingers crossed Jelena! Sounds super cool!

And by „there” I mean session 7.1 in room 141!

It looks like a painting!