Send message to Authors

Short Abstract

This paper introduces a three-loop organisational learning framework, based on relational signals, that helps citizen science teams redress power imbalances, scale collective meaning-making, and govern community knowledge. AI underpins synthesis while enforcing community data sovereignty.

Abstract

This paper presents a new conceptual framework that helps citizen science teams address three common organisational problems: unequal power between communities and decision makers, scaling up meaning-making, and establishing fair rules for using community knowledge. We explain how AI can act as a simple organisational infrastructure that supports careful synthesis while keeping communities in charge, guided by community data principles.

Drawing from organisational learning theory, we present relational signals as a multi-level framework that transforms citizen science into collective sense-making systems. The framework addresses scaling challenges through three interconnected learning loops.

Surface signals capture immediate organisational responses through standardised feedback mechanisms, enabling rapid pattern recognition across diverse project contexts. These authentic emotional and cognitive reactions create organisational memory that can be systematically used and compared across initiatives.

Rooted signals emerge through structured narrative synthesis processes where participants co-construct meaning from their experiences. This represents double-loop learning where communities question underlying assumptions about environmental stewardship, shifting to active knowledge co-creators. The framework facilitates identity transformation at both individual and collective levels, building organisational capacity for sustained engagement.

Branching signals track emergent actions months after project completion, demonstrating how learning transfers beyond project boundaries into autonomous community-driven initiatives. This third-order learning creates self-replicating organisational patterns that scale naturally through networks rather than top-down mandates.

AI serves as a coordinating infrastructure by automating pattern recognition across signals while preserving community agency through transparent algorithms and participatory governance protocols. The framework implements community data sovereignty principles, ensuring communities retain control over their knowledge.