Configuring Collaboration: A Fledgling Digital Civics Research Manifesto

Configuring Collaboration: A Fledgling Digital Civics Research Manifesto

Having recently read Le Dantec’s Strangers at the Gate [9], the importance of researchers setting out their research agenda and guiding principles from the outset is fresh in my mind. So, in keeping with the spirit of that paper here are mine.

I aim to work with local individuals, activist organisations, councils and other stakeholders to improve the quality of both the data and the debate around cycling and safety in the North East. Ultimately I want to encourage local authorities to design and build better walking and cycling infrastructure to enable people to travel in ways that benefit their health and the environment.

You can also find a fuller discussion of my emerging research ideas here, and an assortment of literature that has shaped my interests here [1,2][6][21].

After three months of learning about HCI I’m beginning to understand that nothing is ever quite as straight forward as it seems. What started out in my head as a simple project, putting sensors on bikes and gathering some data to convince the council to build better infrastructure for cycling, has gradually been problemetised beyond recognition. From an initial inclination to take a problem-solving approach that would have made Oulasvirta and Hornbæk[17] proud, via not quite “becoming with” Light and Akama and their “politics of care”[15], I have come to a fuller understanding about the idiosyncratic nuances and challenges of conducting research in the field of HCI.

This blog post aims to articulate some of the key challenges now facing me as I begin to develop my own Digital Civics research agenda. In this post I will identify HCI topics of particular interest and describe how selected literature has helped shape my thinking. I will discuss issues raised by these papers and pose a number of key questions my work must address. I will not attempt to answer these questions here. Instead I will leave them open to inspire further thought and discussion in the months ahead.

I will begin with a discussion of participation, in which I will consider subjects such as the role of the researcher, the identification of publics, and participation vs collaboration. I will then move on to discuss issues linked to data, starting with conceptions of data, issues regarding trust and ambiguity of data and finally, the interaction between data and design. I will conclude by reflecting on the open questions raised throughout the post and use these to create a tentative manifesto for my future research.

Part 1: Participatory everything

Establishing an action research agenda and my role within it

“Researcher, confidant, advocate, interloper, invader, collaborator” As Le Dantec argues in Stranger at the Gates, [9] community-based research necessarily places the researcher in a variety of roles that are not only challenging but at times even contradictory. At the most fundamental level there is a tension between the roles of academic researcher and collaborator. The researcher is bounded by factors such as the field he/she operates in, an existing research agenda (or areas of interest at least), time available and the need to publish novel research material. The collaborator on the other hand seeks to slowly integrate into a community, understand their needs and agenda and create something which addresses their particular concerns to create sustainable change.

Resolving the potential conflict between these many roles and dealing with the overall “messiness” of community-based research is likely to be a central challenge of my work. Action Research is a “class of methods and approaches for conducting democratic and collaborative research with community partners”.[14] It is a collaborative, reflective, open ended and iterative process of inquiry and one that appears well suited to help me navigate this messy space while maintaining scientific rigour.

Some potential questions become immediately apparent for my own work which action research offers a framework to help me consider.

How can I square my own fairly clearly defined research agenda with the potential varying interests of any stakeholders I will collaborate with? How far should I be willing to compromise on my own agenda in order to pursue the interests of the community I work with? How can I promote the sustainability of the project and enable the community to maintain and develop the technology beyond its lifespan?

Identifying publics

In Participation and Publics: Supporting Community Engagement [7] Le Dantec builds on Dewey’s conception of publics. “a public does not exist a priori, it forms through the identification and expression of a common social condition and the action taken by diverse stakeholders to contend with that social condition.” This idea of defining a public around a collaborative attempt to deal with a specific issue is one that really appeals. It immediately begs the question which individuals and groups might make up a public around my research agenda? I have identified three distinct stakeholder groups that I have identified so far: local councils, local cycling groups (including activist groups) and local individuals made up of pretty much anyone who lives locally and has an interest in walking / cycling. But there are questions around how this public should be brought together.

Should I focus on working with those that have the power to make change happen? Or, should I instead focus on activists who share my ideals? Which partners have the skills, tenacity and organisation to ensure sustainability of any intervention? How do individuals, particularly those that are marginalised fit in? How do questions of social justice dictate who I should work with? And finally How do I form a coherent and unified public from an enormous body of relevant people with differing agendas?

These are foundational questions, and as Le Dantec points out, the way I choose to answer them will have profound effects on the outcomes of my research.

Interaction, participation, collaboration

In Participation Claire Bishop distinguishes between participation and collaboration. Participation involves more than just button pressing interactivity, having in addition a social dimension. It “strives to collapse the distinction between the performer and audience, professional and amateur, production and reception”. Participation here has a social and even political aim to reduce these inequalities. All very promising for my research which will depends upon attracting significant numbers of participants. However, Participation is not binary, as Arnstein’s ladder[4] shows and can often be tokenistic. Even more problematically it is not necessarily an unequivocal good.

In his critique of participatory art Include me out[5], Dave Beech argues that participation is the poor cousin of collaboration. Not only that, it can, in fact, come at a significant cost for participants while doing little to change the underlying power structures.

It is the shortfall between participation and collaboration that leads to perennial questions about the degree of choice, control and agency of the participant […] collaborators are distinct from participants insofar as they share authorial rights”

“Outsiders have to pay a higher price for their participation namely, the neutralisation of their difference and the dampening of their powers of subversion. Participation papers over the cracks. The changes we need are structural”.

This view of participation poses a significant conceptual challenge for my project which relies so heavily on participation.  This has given me pause for thought regarding the need for participation to offer benefits for people that take part, rather than simply assuming that participation must be a positive empowering thing.

To further complicate the issue, participation is a particularly thorny issue in the cycling world given that there are two distinct types of cyclists that deserve our attention. There are the “upwardly mobile cyclists” those who choose to cycle and view it as an empowering and “emancipatory” activity. Secondly there are those for whom cycling is quite the reverse, a necessity that is “an outcome of oppression, leaving the bicycle as the only reasonable travel option”.[13] Issues of class, race, wealth and power are clearly central here.  Who could blame those cyclists who only ride a bike out of necessity and aspire to drive a car from saying “Include me out!”. The question of how best to involve those who only cycle out of necessity and may not identify as cyclists is one that I have yet to find a satisfactory means of addressing.

What will participating in this project offer people? And what will it cost them? How do I encourage collaboration? Where collaboration with large numbers of people may not be possible should participation instead be the goal? How do I ensure that this participation works for those involved? How can I work with those who are marginalised and or cycle out of necessity?

The difficulty of answering these questions suggest that as Vines et al.[20] argue the primary work I will be engaged in as an HCI researcher is not participatory design but the act of configuring participation.

Part 2: Data, Design and Digital Civics

All you need is love data

According to some, the deluge of data in the modern world has made the scientific method obsolete; rather than theorising, all we need to do is collect and analyse huge amounts of data. “Who knows why people do what they do? The point is they do it, and we can track it and measure it with unprecedented fidelity. With enough data the numbers speak for themselves”. [3] For my own project, which could be viewed as a type of data activism, (albeit with small rather than big data) the implication seems clear: all I need to do is collect loads of data and let the numbers tell a persuasive story. It all sounds very simple.

But in Data and Life on the Street [18] Alex Taylor brings some realism to the discussion of what can and can’t be achieved with data. Taylor’s interest in data, is not related to how it can solve large abstract problems but rather how it “could come to matter in a place and amongst people with some very real ideas and concerns”. This local, realist conception of data fits closely with the Digital Civics agenda[16] and has been a valuable learning resource.

Making data make sense

Taylor’s paper also discusses how data can be conceived of in various ways depending on one’s relationship to and understanding of the data in question. Several of his participants conceived of the data they collected as “ammunition” or “evidence” that can change people’s minds and inspire action. One even coined the catchy soundbite “The purpose of data is action”. This view of data has echoes of the description I gave at the start of this article of how I see data being used in my project. But put like that it started to trouble me.

What if the action that the data inspires is not positive action? What if the data is misleading and it inspires the wrong type of action? How do we prevent this from happening? Can the purpose of data also be to increase reflection, understanding and dialogue?

In data we trust

A related point is that of neutrality. Taylor describes how participants viewed researchers as “people who don’t have an axe to grind about the data, you just want it to flow”. This trust, of the researcher and by extension the data seems to me a crucial question. Taylor’s participants also identified “data coming from us” vs “data done to us”. The participants naturally trusted “their” data more than external data.

If I am consider myself an activist and set out to create an explicitly persuasive technology [10,11] then wouldn’t the council be justified in treating it with suspicion? Should I instead seek to work directly with the council to create something that they are interested in and trust? What would that mean for my research agenda? Would it be possible to bring the council and individuals and activist groups into one space to effectively collaborate as a public? Or would the opposite approach – one of agonistic pluralism such as seen in Million Dollar Blocks be more effective?

Here’s one Le Dantec made earlier

In Planning with Crowdsourced Data[8] Le Dantec discusses the Cycle Atlanta project where GPS data was collected from cyclists to visualise which routes they ride. This data was then used at a planning workshop or “charrette” where the researchers were able to witness how council officials and members of the public responded to it. One of the ways that the data was viewed was as “authority”. It was “not viewed as fallible as routes drawn from memory or from idealised preferences” and there was a perception that it represented “truth and fact”. This in turn made the data a powerful “source for argumentation” – something that could be used to make the case for existing or new infrastructure. This was despite the fact that there were several clear shortcomings with the data and several of these had been explicitly mentioned to attendees.

How can the chance of the data being misunderstood or over-interpreted be minimised? How can I make it so that the shortcomings of the tool are clear while still preserving the utility of the interface?

I really enjoyed reading the paper and felt that there were clear lessons to be learned. Firstly, although it was mentioned to participants the interface itself did not make explicit the biases, ambiguities or problems associated with the data (such as lack of participation from certain demographics such as poor and non-white neighbourhoods). The simple, clear intuitive interface enabled easy interpretation of the data – a degree of interpretation that it could be argued the data did not justify. That’s not to say the technology here is without value (quite the reverse I think it’s a fantastic tool) but the design of the interface does not articulate the fact that there are alternate ways to conceive of the data and I think this contributes to the issues described in the paper with the data becoming “authority”.

To give just one potential design alternative, it would be possible to show only start and end points of journeys with only straight lines between the points. This would ambiguate the routes that were taken and force the user to interpret potential routes. For a planner this might mean identifying the potential for a new route rather than focussing on improving one that appears to be popular only because it is enforced by the current infrastructure.

Can ambiguity help to prevent over interpretation of data?[12] Might multiple differing views of the data highlight its ambiguity, provoke further thought and lead to better understanding of the underlying issues? Could I integrate existing data such as police accident data into the system to encourage thinking about the issues from other perspectives? How do questions of design affect interpretation of the data?

Conclusion 500

In this blog post I have touched on several areas of interest to both HCI research and my own research agenda. In particular, I have considered what selected literature has to teach me about configuring participation and collecting and making sense of data. I have then set out a series of open questions for consideration. In an effort to make sense of the 28 questions that I identified throughout the blog post I decided to mind map them and to use them to inform a manifesto or set of guiding principles for my future research.

I’ve always liked the way that the Hippocratic oath sets out an absolute minimum standard for doctors to hold themselves to. This is my version, my Digital Civics commitments or a Digitial Civocratic Oath if you like…

  1. Make change but don’t make things worse

Do make it easier for local authorities to save lives and improve civic life by enabling them to understand the hidden needs of and dangers for cyclists and pedestrians. Don’t for example, create a system that facilitates over interpretation of data, a means of justifying existing decisions or misallocation of public money.

  1. Configure collaboration

When working to construct a public be open and upfront about my agenda. Whether I work with the system or against it, I need to create a genuine partnership with shared decision making and explicit aims and expectations. When encouraging participation avoid tokenism – make it meaningful and rewarding.

  1. Make sure it is sustainable

Either create a valuable piece of technology that can be owned, developed and maintained by local communities after my research has finished or quite frankly, don’t even bother. This means a long term commitment to those I work with.

  1. Make it transferable

Make both the learning and technology from this work transferable. Other publics, not just academics, must be able to build on it.

  1. Have some fun

Because life is too short and it’s nearly Christmas.

Ok, it’s not as snappy as the Hippocratic oath and is still a work in progress. I’m sure my plans and thinking will change as I learn more about the realities of carrying out HCI research in the wild [19] and I will need to revisit these guidelines regularly. As it currently stands though if my research fails to fulfil any one of these five aims I know that I will be less than 100% satisfied.

I’ve tried to think of any research ideas that would fit would fit these guidelines that I wouldn’t be happy to work on, so far without success. That got me thinking, perhaps I could share my guidelines with collaborators from the start so they appreciate where I stand. This would not only help us to decide if we can work but define a shared space for us to explore and share the conception of ideas. Perhaps we could take this a stage further and work with stakeholders or potential collaborators to come up with shared guidelines that work for all of us. On the other hand, perhaps I’m overthinking this. I have a sneaking suspicion that I just need to go to bed and get some sleep. Either way, I’ll look forward to continuing to develop these ideas in a non-sleep deprived way in 2017.

  1. John Adams. 2001. The Social Consequences of Hypermobility. Text of an RSA lecture, November: 1–10. Retrieved November 21, 2016 from
  2. John Adams. 2015. Cycling and Safety: Change must take root in people’s minds. World Transport Policy and Practice 213: 10–22. Retrieved December 15, 2016 from
  3. Chris Anderson. 2008. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired. Retrieved from
  4. Arnstein and Sherry. 1969. A Ladder of Citizen Participation. JAIP 35, 4: 216–224.
  5. Dave Beech. 2008. Include me out! Art Monthly. Retrieved from Art Monthly –
  6. Mikael Colville-Andersen. The Arrogance of Space – Paris, Calgary, Tokyo. – Bicycle Culture by Design: . Retrieved December 15, 2016 from
  7. Christopher A Le Dantec. Participation and Publics: Supporting Community Engagement.
  8. Christopher A Le Dantec, Mariam Asad, Aditi Misra, and Kari E Watkins. Planning with Crowdsourced Data: Rhetoric and Representation in Transportation Planning.
  9. Christopher A Le Dantec and Sarah Fox. Strangers at the Gate: Gaining Access, Building Rapport, and Co-Constructing Community-Based Research.
  10. Bj Fogg. A Behavior Model for Persuasive Design. Retrieved November 23, 2016 from
  11. Bj Fogg. Creating Persuasive Technologies: An Eight-Step Design Process. Retrieved November 23, 2016 from
  12. W.W. Gaver, J. Beaver, and S. Benford. 2003. Ambiguity as a resource for design. SIGCHI conference on Human factors in computing systems, 5: 233–240.
  13. Aaron Golub, Melody L. Hoffmann, Adonia E. Lugo, and Gerardo F. Sandoval. 2016. Bicycle Justice and Urban Transformation: Biking for all? 348.
  14. Gillian R Hayes. 2011. The Relationship of Action Research to Human-Computer Interaction. ACM Trans. Comput.-Hum. Interact. Article 18, 20.
  15. Ann Light and Yoko Akama. Structuring Future Social Relations: The Politics of Care in Participatory Practice.
  16. Patrick Olivier and Peter Wright. 2015. Interactions – Digital Civics. Interactions Magazine. Retrieved from
  17. Antti Oulasvirta and Kasper Hornbaek. HCI Research as Problem-Solving.
  18. Alex S Taylor, Siân Lindley, Tim Regan, and David Sweeney. Data and life on the street.
  19. Nick Taylor, Keith Cheverst, Peter Wright, and Patrick Olivier. Leaving the Wild: Lessons from Community Technology Handovers.
  20. John Vines, Rachel Clarke, Peter Wright, John Mccarthy, and Patrick Olivier. Configuring Participation: On How We Involve People In Design.
  21. I. Walker. 2006. Overtaking bicycles in the UK: Cars versus white light-goods vehicles.

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