In-situ assessment report of citizen local interactions and self-reporting GEAR cycle 2

Résumé 0

In this report, we present a data-driven approach leveraging digital traces to introduce and monitor metrics pertaining to the diversity, collaboration and activity of teams, and show how these metrics correlate with outcome and process centric criteria of team performance in the context of the GEAR cycle 2. We collected compositional and organizational data from 14 participating teams from different sources: communication data from public Slack channels, surveys, and self-reported activity data from CoSo (Collaborative Sonar), a digital platform for monitoring team collaborations and task management. We then computed four categories of team features, related to team composition (size, background diversity, prior experience with citizen science projects), communication activity (number of posts on Slack, communication with organizers, closeness centrality in the communication network), collaborations (intra-team work interactions, amount and diversity of the advice seeking ties), and teamwork (amount and span of activities performed, regularity of work). Performance was assessed through the final stage achieved by a team, an objective measure of achievement for the team, the quality of the project (relevance to the SDGs, novelty) as judged by experts, and the quality of the process (engagement, deliverables) as judged by the organizing team. Finally, we tested the associations between performance criteria and team features using Pearson's correlations. We found that team composition and activity on Slack are associated with performance measures related to the outcome of the project, while team collaborations and division of labor associates with performance measures related to the elaboration process. We conclude that measures of team composition, activity and collaborations can be leveraged to monitor team performance pertaining to both the project outcome and its elaboration process, highlighting their usefulness in the context of the Crowd4SDG project and similar initiatives leveraging Citizen Science to address the SDGs.