Sociology
The Construction of the 'Expert' in Citizen Science Initiatives
Quick fact
In many citizen science projects, volunteers collect data but are rarely credited as co-authors; their role as 'expert helpers' is often invisible in formal scientific outputs.
Why this is interesting
Have you ever reported a missing pothole or counted birds in your backyard? Did that make you a scientist, or just a helper?
Read the full explanation
Understanding The Construction of the 'Expert' in Citizen Science Initiatives
Imagine a large citizen science initiative where thousands of volunteers upload photos of plants or record rainfall. Each volunteer contributes an observation, but who decides whether those observations are 'scientific data'? The answer depends on the construction of the 'expert.' Initially, the volunteer is a layperson, but through the act of contributing to a scientific project, they might be considered a 'citizen scientist.' However, being an 'expert' in this context is not automatic. Often, the project provides training modules or toolkits that standardize how volunteers observe and record data. This training transforms the volunteer into a 'trained observer'—a kind of proto-expert. Yet, their expertise is often limited to data collection; they don't interpret findings or design the study. The construction of the expert therefore involves a negotiation between the volunteer’s local knowledge (e.g., noticing a change in bird behavior) and the scientific protocol's demands for objectivity. The organization may rebrand volunteers as 'community partners,' but the power to define what counts as knowledge often remains with the professional scientists. Step by step, the construction process involves: (1) recruiting volunteers, (2) providing training and tools, (3) collecting and standardizing data, and (4) deciding how much decision-making power volunteers receive. At each stage, the 'expert' label is contested—some volunteers gain recognition as experts, while others remain 'helpers.'
A deeper explanation
The construction of the 'expert' in citizen science is a social process that draws on the concept of 'boundary work.' Professional scientists maintain a boundary between themselves and volunteers by controlling the scientific validity bar. This is not just a technical matter; it's about authority. The mechanism works through three intertwined processes: 1. Credentialing: Within a citizen science project, expertise is often informal. There are no degrees, so credibility is built through participation, reliability, and adherence to protocols. This is a form of 'soft credentialing'— volunteers earn trust through their contribution record. 2. Epistemic boundary drawing: Scientists decide what counts as data. For example, a volunteer's narrative observation may be considered anecdotal, while a quantitative measurement (like a count) is elevated. This boundary drawing can marginalize volunteers who possess rich local knowledge but don't format it correctly. 3. Institutional recognition: The project’s institution (e.g., a university) legitimates the volunteer's work through acknowledgment, but rarely grants true co-authorship. This creates a hierarchy where volunteers are 'data collectors' and scientists are 'data interpreters.' This construction is not malicious; it is rooted in concerns about data quality and scientific rigor. However, it means that 'expert' is not a natural category but a relational one—defined by the project's norms and the scientists' authority. Understanding this helps us see why some citizen science projects succeed in empowering communities (by recognizing local expertise) while others merely extract data. The construction of the expert is thus a power dynamic, shaping who gets to speak for nature.