Public Sector Innovation Labs Blog 2: Experiments in Transformative Learning + Scaling Deep

Niho taniwha (Chevron skink), photo by Rod Morris

In our previous blog we gathered up our shared insights about transformative learning and scaling deep from working across two different public sector innovation (PSI) labs, the Solutions Lab in the City of Vancouver and The Auckland Co-Design Lab in Aotearoa New Zealand. We articulated an additional PSI lab archetype focused on transformative learning and scaling deep, and six moves that public sector innovators and lab practitioners might consider as a pathway to deepening the impacts of lab work.

In both of these cases the focus on scaling deep and transformative learning evolved over time, and through efforts to ensure and evaluate the potential impact of different approaches. This blog post provides some more about the journey of each labs’ shift towards transformative learning processes and some specific examples of what these shifts looked like in our different settings.

We are deeply grateful for our colleagues and collaborators in our respective labs, organizations, and communities, and we hope that the ways that we’ve gathered up this thinking honour what we’ve learned together alongside each of you.

You may be interested in this article if…

You are exploring the potentials and practices of transformative learning and/or scaling in your own public sector and/or social innovation work.

You’re a public sector innovator and/or lab practitioner who is interested in how a learning-oriented approach — at the individual, relational, and systems scales — might support your practice.

You enjoy reading about how other public sector innovators and labs are reflecting on their own practice.

There are two main sections in this post where we share specific examples of shifts toward transformative learning and scaling deep — first from Vancouver’s Solutions Lab, followed by Auckland’s Co-Design Lab.

Read more at Public Sector Innovation Labs: Experiments in Transformative Learning + Scaling Deep

Read the first blog post here