Phase 2: Co-authorship and publication
Engagement with the platform / paper began at different stages of each day of each workshop for different people. Sections were predefined according to typical sections.
Background The design team including academics would clear the previous day’s writing and begin to develop a background section. We decided that all background literature that foregrounded the activities and their analysis, would only be derived from the talks and presentations that occurred on that day within Future Everything. This meant two/three team members sitting in panels and streams developing text that supported emerging themes that were present within the Latex paper. Often in different rooms whist this took place, there was no time to discuss themes or particular narratives – and only the opportunity to mediate and develop these throughout the paper writing process. On the first day the team spent a considerable amount of time describing the methods and background to the actual workshop, our intentions and tactics so material from the conference was limited. By the second day the team chose to repurpose these aspects to allow more of us to attend the conference and draw out more interesting theoretical material and related projects. At various points in the day social media was also used to encourage other conference delegates to contribute to the paper. A good example was Tom de Grunwald’s contribution that occurred through conversation and an email submission that was sent to myself following his interest in our process: “The paper, both in content and form, participates in the emerging area of co-owned, permission based data sharing, as exemplfied by Open Paths , an initiative of the The New York Times Research & Development group .” Tom de Grunwald, Co-Author, paper 1.
- OpenPaths https://openpaths.cc/ [Accessed 26 February 2015.]
- nytlabs http://nytlabs.com/projects/openpaths.html [Accessed 26 February 2015.]
Methodology, Participants, Data Collection and Analysis Following a rationale for the methods that we were using for the workshop the paper pulled material from the questionnaire that participants completed at the beginning of their involvement with the workshop. Serverside calculations were made that fed directly into the paper: “All participants were familiar with technology, owned digital cameras, and 86.6667% cycle at least once a week.” “15.3846% cycle mainly for leisure, 0.0000% cycle for mainly sport, whilst 7.6923% cycle for short trips (e.g. shopping) purposes.” Of course the sums need some work, the aggregation of commuters led to over 100%: “Out of the participants who cycled, 114.2857% cycle mainly for commuting purposes”. Despite these glitches, the opportunity to integrate quantitative data into a paper which was updating all of the time, as people continued to complete the form was very exciting, and allowed the numbers to gain a level of performativity which inflected the balance of the writing.
Participants were encouraged to write into the three sections that made up the Findings. Sub headings of these sections were decided as the days went along and according to themes that were emerging and participants chose which one to edit depending upon these themes and the comments that other authors were writing into each section. Interesting examples of editing can be found in paper one that involved the academic speculating upon messages sent from the Comob application that were then extended by the participant: Academic: Blue Participant: Red
Participants X commented how water resists resistance perhaps as though the presence of water on the road surface impeded braking, or something more about how rain affects an ability to take control of the road and resist the hegemony of the car. X stopped by the river, watched it beat waves towards the riverbanks, how it couldn’t resist the pull of downstream.
The participants extension suggests a misreading by the academic and instead offers a more poetic, less descriptive perspective. Participant W was much bolder in qualifying what was meant by his message:
Other materials on the road were highlighted by participant W who simply wrote ”horse shit!” and later explained the dangers of animal excrement that is often found on road surfaces. Participant W: “Autocomplete put the words in my mouth. What I actually screamed was ”Holy crap!””. Things in the way, physically and unseen, the pollution filling my lungs.
In paper two, 3 participants spent a considerable amount of time at laptops at the workshop HQ describing their experiences during the afternoon. Participant A made suggestions for the use of technology to highlight difficult roads:
The biggest problems for me are having very narrow roads where parked cars are on the left of the road with cars on the right going past very fast; and also junctions with multiple lanes of traffic and having to change lanes. If the data could show which routes avoided these roads / junctions it would go towards helping show what the most safe routes are. If the cyclist had sensors in front, behind, left and right, it would be interesting to see how much space cyclists have around them on different routes.
Participant C developed a reflection upon existing data capture from Google as well as an opportunity for gathering data more relevant for cyclists, whilst on the back of a tandem:
The notion of crowd sourced cycle and transport data emerged whilst I was being steered around the city by my tandem companion. Google’s turn by turn navigation system uses massively crowd sourced data coming from its Android operating system to provide live information about traffic flow rates, this allows car drivers to circumnavigate traffic black spots. Similarly Google’s Streetview project not only allows users to view any included location photogrpahically, but Google’s back-end systems analyse the images in order to populate their address databases and other services. Their Re:Capture system is used to verify information such as door numbers and building name signs. Similar systems could certainly be applied to cycles: collecting data from cycle-mounted cameras and GPS devices and using that data to add value to other services. For instance the evasive action taken to avoid potholes could be shared, live, with cycles approaching that particular pothole. Potentially drivers of motor vehicles could be warned of approaching cyclists through cycle-to-car data links.
Participant E delved into the social interactions that they experienced in the city and in particular the frictions felt in the relationships with other roads users :
When we collided with a pedestrian, we cycled back around to apologise. He waved his hand and gave a wry smile. Setting off when the lights go green is often nerve wracking, will the vehicles behind give you space? If turning right then being stuck in the middle of the road, with moving vehicles on either side, waiting for a gap in the traffic is intimidating. Motor vehicles frequently do not respect the ‘advance stop’ boxes. I wonder whether they know the reason why I deliberately manouvure my cycle around their vehicle, stop in front of them, and turn around to glance at them?
Discussion and implications The discussion and implications part of each paper drew together the themes within the paper that had emerged both from the conference discussions and the findings gathered from participants and academics reflections. It also allowed space for reflection on the co-authoring methods that led to the paper, and used the closing panel to summaries both participant experiences and invite guests to discuss implications.