You can use impact stories to report on changes that result from your Citizen Observatory. An impact story is a narrative about your journey from when you started the Citizen Observatory. In your story, you will explain the issue you were trying to tackle and talk about moments or events when you were aware of a change that had occurred in relation to the problem you were working on. For example, you can use an impact story to report on a moment or event where you felt empowered in a discussion with authorities because you had gathered data and insights through your work as a citizen scientist. The WeObserve Impact Community of Practice developed an approach that focuses on capturing and promoting policy impacts. The Impact Inquiry Instrument is available here.
Example from the Making Sense project
For Making Sense, participatory evaluation was crucial to the development of the project and also the participants’ awareness of what they had learned about the environmental challenge. During a campaign in Barcelona, the participants got together for a workshop on CLI. They first agreed on a sensing strategy, which included where and when they would be collecting data on noise pollution using the Smart Citizen Kit.
They then used the CLI tool and worksheet to collectively decide on one or two other indicators that could be used for data annotation in combination with the sensor strategy. The workshop helped participants think about the problem differently. For example, some of the participants wanted to monitor the public presence in the area where the data on noise levels was being collected and then compare this information to the noise sensor data.
This allowed participants to overcome a culture of blame and to instead see the possibilities of collaborative decision-making. Also, the CLI workshop helped them make sense of the sensor data. By discussing the CLIs the participants were able to plan approaches that would build on the sensor’s datasets to reveal deeper insights into the issue of noise pollution.
In one case, a participant used her phone to photograph the number of people on the street. These images evidenced the source of the problem. Once the community had gathered the sensor data, they used these photographs alongside the data sets to demonstrate the problem to local government officials.