Strategic research in vast complexity
Lessons learnt from exploring the future of Free Knowledge for Wikimedia Germany.
Unlock your ideas!
We are very excited to see a new project come to life: the UNLOCK accelerator by Wikimedia Germany just launched last week. It’s a dedicated initiative to promote the advancement of „Free Knowledge“ in the digital age. Part of Wikimedia’s global strategy 2030, UNLOCK supports teams with ideas on how to tackle the tricky challenges of a complex knowledge society in an open, collaborative, and free way support and speed — through funding, coaching, and their vast network.
Over the past three months we had the chance to be part of shaping the conceptual framework of the five thematic fields in the accelerator. We conducted interviews, concept deep dives, and explorative research sessions to derive five tangible levers to advance a free and digital knowledge society. It was a daunting, challenging, and fun journey that led us into the cosmos of Wikimedia itself as well as to open science activists, African start ups building digital commons, a very different and genuine innovation and learning culture in China, and the interplay of politics, education, and knowledge in Europe. While — expectedly — there remain many open and exciting questions, we narrowed down this extremely vast field to five distinctive opportunity fields:
- Networks & Infrastructure — Building an interconnected backbone of frameworks, data, and formats, with navigating flexibility and reliability and structure set up.
- Competencies & Skills — Tackling very diverse skill sets for very diverse types of knowledge, from hands-on, to academic, to embodied, to documented.
- Horizons & Bubbles — Exploring the challenges, synergies, and opportunities of contexts, situations, communities, and identities when to discussing facts, stories, history, and more.
- Knowledge Production — Highlighting the conditions under which knowledge emerges, including fair renumeration, direct and indirect power dynamics, reciprocity, and open source business models
- Knowledge Futures — Looking ahead into a desirable future of collaborative and free knowledge exchange that includes a join narrative for effective collective action.
Challenges and Ambitions
While the (alas, for now in German only) report is now available online (with an english version slated to come soon!), we wanted to take the chance to quickly share not the results, but the process of our project. How and where did we start? How did we work? How to tackle such a vast topic with limited time? And what did we learn our selves? To begin with, there were a couple of distinct challenges with this project:
- Vast Complexity: The theme “The Future of Free Knowledge” is virtually endless. It reaches from the distinction of Free and Libre to data, information, and privacy, to epistemological questions, to future scenarios for global collaboration, to many follow up questions around what matters, who matters, what even to aim for, and where to start. We knew that as a consequence any results had to remain selective and preliminary.
- Strong Opinions: We were anything but the first to look at these questions. The quest for building a digital and open knowledge society is at least as old as the internet itself, with many well-experienced organisations, activists, and projects having their share of the past decades — with Wikipedia itself being one of the most prominent ones. With so much back story comes a lot of history, positive and negative, emotional, political, and cultural. For us, care, respect, and consideration was needed to explore this in any fruitful way.
- Systemic Uncertainty: There was another challenge in conducting the research: While we all agreed, that we would like to approach this field as openly as possible, this meant that we had close to no boundary, conceptional constraint or guiding principle to start off with. Everything had to be presumed in order to discover it. While this worked in a smaller team, the challenge often lied in communicating and discussing seemingly arbitrary categories with our partners and supporters, before we could validate the categories in the first place. A classic chicken-and-egg problem that often comes with open research, especially when directed at a future scenario or outcome.
What we did, what we learned
We went for a qualitative approach with few, selected interviews, rather than aiming for an “exhaustive” or“representative” study of quantitative insights (because we believe in the field of Free Knowledge, there is almost no such thing as exhaustive or representative). Our focus laid in finding our own perspective, deciding on our own priorities and making a case for them. Against this light, here come a few take aways, best practices, and lessons learnt from an ambitious project:
1. Making our own assumptions explicit
As a very first step, we unpacked everything we had in mind ourselves. Our team was excited about the project and full of hopes, early ideas, expectations and questions. So we took the time to write it all down and look at our own assumptions. This did not just include us, but also the client’s team. What we ended up with was a fairly tangled yet exhaustive collection of (very) early questions and answers. It allowed us to cluster a first set of themes, to tie them back to existing projects and past learnings, and to explicitly look for blind spots and uncovered ground. We kept repeating these sessions, mapping everything we had heard and challenging it with the client’s team (and even their coworkers) to refine our broad categories into more nuanced ones. As a side note from a more epistemological point of view: One of the many paradoxes of knowledge is that, in a way, you need to know — or at least expect — the answer before you ask the question. We tried to cater to this fun loop by doing just that.
One of the paradoxes of knowledge is that you often need to know the answer before you can ask the question.
2. Radically open co-creation
We were extremely lucky to have a great team on Wikimedia’s end with us — as well as some truly wonderful and inspiring experts for our interviews. That allowed us to basically work in the open: We conducted all of work, syntheses, collection of data, unpacking of interviews, and even internal discussions on a shared Miro board. This board was notepad, client presentation, communication channel and brain dump in one. While this spared us a lot of back and forth (and prepped us well for the transition into a full on remote working mode) it also allowed for more surprising interventions, spontaneous questions and comments by others, and an organic “project history”for everyone to follow up upon.
3. Deep Dives
With a lot of individual input, best practices and stories from our interview partners, we regularly conducted deep dives into methodological, epistemological, or sociological fields. This helped by providing robust frameworks for further categorization — and it inspired us to ask new and different questions further on. It led to things becoming quite complex from time to time, but also allowed to make sure we don’t overlook any gaps, while keeping us grounded to theoretical and practical insights we heard and learned so far.
4. Coping with complexity
Talking about which. Of course we, too, at one point felt quite overwhelmed by the sheer amount of data, interdependencies, important questions, challenges, and implications of our research. It took several iterations of distilling our data down into more or less concise angles. And, we’re not gonna lie, that was painful. With every iteration it felt like we’re leaving out something important, we’re oversimplifying or missing a connection. It was incredibly helpful to have critical feedback sessions with the Unlock team, as they served as the “users’ eye”, testing the narrative, the clarity, and the scope of our results. This made us go back to the drawing board several times, taking another lap of distilling things down and gaining clarity.
Another thing that helped at this latter stage was the introduction of clear categories to make our clusters comparable. You can find the recurring questions in the final report: “What is it about”, “What’s the challenge” “What is needed” and “What is there already”. Here, too, structure and substance, or form and function co-evolved iteratively.
It’s a back and forth between inventing structure — applying it to your data— and refining the structure based on what you find out. In other words: it is designing knowledge.
5. Drawing the line
Given such an open-ended question — where do you draw the line? Apart from the very real project and time constraints with launch of the program itself, this research proved to be kind of a luxury: While we provided a first digest of things and themes that stood out to us over the past months, the report is now published on the meta-wiki of the accelerator itself. That means is it very much a living document. The project and the topic itself comes full circle here, inviting everyone to take the deep dive themselves, read through the report, and adding, editing, and building upon it. Who knows, maybe this marks the basis of the next iteration. We, for one, sure would be excited to keep learning.
We thank the wonderful team at Wikimedia Germany e.V. for an inspiring project, the great friends and partners who shared their insights in interviews and mails with us, and we can’t wait to see what’s coming next — through the accelerator and beyond!
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