Literature abounds with conceptual models of Citizen Science (CS) projects. They can be categorised according to levels of participation (Haklay 2013), sensor deployment (broad v slim), agency (protocol-based v full autonomy). Existing models can be extended - for instance, by adding ownership of a CS lab (level five) to Haklay’s typology - to capture new roles and opportunities in a constantly evolving space.
The flurry of activity in academic literature is mirrored on the ground as many countries are witnessing a real boom in CS activity. Based on original research conducted by COMPAIR, there are countries whose landscapes are fairly modest, comprising just several dozen initiatives, most of them ‘external’, and then there are those with a much more advanced ecosystem, characterised by a strong presence of domestic projects, ‘supporters’ and ‘inside-out’ initiatives i.e. domestic projects whose influence transcends national boundaries.
To the best of our knowledge, and based on a quick desktop survey, no attempts have been made to unite the two models to create a typology of CS regimes. We believe that two preconditions for this new theoretical framework have been met, namely the abundance of different modes of participation and the diversity of CS project types found in different countries. In the next few paragraphs we would like to sketch the basic contours of this typology, starting with its objective.
For a given country, the typology would show the distribution of projects across a range of disciplines. After mapping all initiatives according to type (external or domestic) and level of participation (low or high), the quadrant with the biggest project cluster would determine whether the national CS regime is predominantly external-low, external-high, domestic-low or domestic-high.
The COMPAIR typology of CS regimes
External-low CS regime: Characterised by a large presence of ‘external’ or ‘outside-in’ projects (international, European, EU-funded) that offer low levels of engagement i.e. citizens act as sensors, interpreters or basic observers. Examples include eBird and iNaturalist.
External-high CS regime: Similar projects dominate this landscape as in the previous regime, with the main difference being that these projects offer a more meaningful engagement to citizens i.e. citizens can help define a problem, build sensors, collect and interpret data, maybe even co-manage a CS lab. Examples include WeCount and Citiclops.
Domestic-low CS regime: The landscape has considerably more domestic projects than in two previous regimes. Some of these projects may even be ‘inside-out’ initiatives with branches and/or sensor deployments in many other countries. However, most projects of this type offer only basic engagement to citizens. Examples include AstroSounds, Alientoma and Sensor.Community.
Domestic-high CS regime: The fourth type is characterised by a significant presence of domestic projects, ‘supporters’ that nurture the ecosystem and ‘inside-out’ initiatives all offering deep and meaningful engagement. Examples include HASSELair and Open Soil Atlas.
One hypothesis that requires testing is whether regimes evolve from external-low to domestic-high? In other words, from arguably a more basic landscape (dominated by external projects offering low levels of participation) to a more advanced landscape (dominated by domestic projects, including ‘inside-out’ initiatives, that offer more meaningful engagement)—under the influence of different drivers of change. These can be national policies that stimulate public participation in science, national funding for CS, technological advances, activities performed by enabling and supporting initiatives, and more.
Another hypothesis concerns external-high and domestic-low, can these be considered regimes in transition, with demestic-high being the ultimate goal? Or can these two types be considered fully self-contained, whose existence is not driven by any need or objective to evolve into something else?
To understand which of the four regimes are present in a country, a significant share of CS projects would need to be mapped along the x and y axis. To get a better sense of where the regime is now compared to where it was before, finished and current projects can be mapped separately, with distributions then compared to identify possible changes over time.
Another idea would be to perform two mapping exercises with a time gap of several years to see by how much the regime has changed during this period. It’s possible that in some domains (e.g. biodiversity monitoring) the country’s regime is domestic-low or domestic-high, while in others it is dominated by external projects with high/low levels of participation. Where this is the case, it would be interesting to understand why some domains ‘outperform’ others, is it due to funding, policies, societal norms, or something else?
The same is also true for the typology as a whole. Future research seeking to validate the framework would need to provide possible explanations as to why the country’s regime is what it is, and how to improve it to get to domestic-high (assuming it’s the goal), or how others can get to this stage if domestic-high has been achieved. This deliverable lays the foundation for precisely this kind of work that we intended to complete in the future.
 Oudheusden, M. V., Huyse, H., Laer, J. V., Duerinckx, A., & Soen, V. (2021). Sharing open science experiences: A conversation on citizen science . In proceedings 2021. https://doi.org/10.21428/1192f2f8.c6029b3b  The inside-out version of projects have really only been available in the past 20-30 years due to technological progress. Before that, most projects were domestic. So technological advances are clearly an important driver that have made inside-out initiatives possible.  For an example of an ‘enabler’ see the Greek chapter, for ‘supporter’ Belgian and German ones