Transcontextual learning, warm data and unforeseen connections

Abstract

This article explores practices of trans-contextual learning, warm data, making unforeseen connections as approaches to address the complexities of interconnected systems. Trans-contextual learning involves collaborative, adaptive, and feedback-informed learning across different contexts and disciplines. It emphasises breaking down silos to find innovative and sustainable ways to go on. Warm Data Labs, developed by Nora Bateson fosters relational in- and outsights and emergent understandings by engaging participants in non-hierarchical, reflective, and dialogic processes. It seeks to move beyond reductionist and mechanistic approaches to embrace the complexity of interconnected systems. Transformation happens in unforeseen connections within a field of many possibilities. Trans-contextual learning might be crucial in the face of what is described as a “poly-crisis” — multiple, overlapping crises with cascading effects. People concerned explore how they collaboratively, trans-contextually learn, step by step, stumbling, and feedback informed, in complex systems, within multiple contexts.