Eran Schwartzfuchs

When Science and Society Meet

In the basement of the Steinhardt Museum of Natural History at Tel Aviv University, two seemingly contradictory processes unfold every day. Behind closed doors, scientists carefully review data collection protocols and deliberate over every aspect of the research process. As in most scientific domains, their main concern is ensuring the quality of the knowledge produced. Meanwhile, the people collecting that data—from elementary school children to older adults—record observations of organisms using their phones, whether in the field or their backyard, marveling at nature as they go.

Citizen science is a research methodology in which members of the general public—who are not professionally trained scientists—take part in scientific research. They assist scientists with collecting, organizing, and analyzing data. In recent years, digital platforms have helped make citizen science a major method for data collection in global ecological research. According to the GBIF database, over 9,000 scientific articles in the fields of ecology and biology have been published based on data gathered through citizen science projects.

The Israeli Center for Citizen Science was established in 2021 at the Steinhardt Museum of Natural History at Tel Aviv University to coordinate, organize, initiate, and lead Israeli projects in the field. The Center operates digital platforms such as iNaturalist in Israel, which allow participants to report observations of various organisms.

The contrast described above—between scientific rigor and public participation—reflects a core tension in citizen science. This tension is prominent in the scholarly literature on the subject, which tends to split into two main perspectives: romanticized on the one hand, and hyper-critical on the other. The romantic view sees citizen science as a successful endeavor that democratizes science, includes the public, and dismantles knowledge hierarchies by opening the doors of science to non-experts. The hyper-critical perspectives argue that citizen science fails in its promises. From the “hard sciences,” critics claim it is not sufficiently scientific, pointing to the expertise, tools, and knowledge required for rigorous research and the lack thereof in public-driven data. From the social sciences and humanities, critics argue it is not truly democratic, as it reproduces scientific hierarchies, excludes certain populations, and exploits public “volunteering” for symbolic and economic gain. As an anthropologist, this tension formed the basis of my research question: rather than asking whether citizen science bridges the gap between science and society, I examined how citizen science is produced through the ongoing work of managing that very tension.

Over the course of a year, I conducted an ethnographic study of the Center’s daily work through observations, interviews, and analysis of documents and digital platforms. I examined how the team manages the tension between three core aspirations:

1. Scientific validity – ensuring the scientific quality of the data collected.
2. Public engagement – building meaningful relationships with participants and ensuring a positive experience.
3. FAIR principles – a widely accepted set of criteria for fair and open data management.

How Does Data Become Valid?

One of the main findings of my research reveals how the Israeli Center for Citizen Science addresses the challenge of scientific validity. In order for data collected by members of the public to be accepted as reliable by the scientific community, the Center relies on four key mechanisms:

1. Validation through technology: The capabilities of smartphones—documenting time, location, and high-quality images—enable observation reports that do not rely on the user’s expertise. The strength of citizen science lies precisely in its technological accessibility and its integration with participants’ everyday lives. As one researcher put it:
   “The power of public participation doesn’t come from years of academic study but from everyday life. People go on walks, they see flowers, they photograph the flowers and upload them. This is something they already do in their daily lives, and we use that for science.”
2. Validation through protocols: Structured and simple protocols allow non-scientists to collect data consistently and uniformly. These protocols act as “boundary objects”—a term coined by researchers Star and Griesemer—that help bridge the scientific world and the general public.
3. Validation through community approval: On iNaturalist, observations are verified by other users, including experts, who identify and confirm the species recorded. Only observations that reach “research grade” are included in official databases.
4. Data cleaning: Finally, before data is used for research, scientists perform data cleaning—filtering out errors and problematic observations.

Involvement and “Wearing Hats”

While data cleaning is standard in any data-based science, in citizen science it is often accompanied by guilt and ambivalence, as expressed by one researcher during the process:
“I feel uncomfortable. On the one hand, we encourage the public to participate and record as much as they can… these participants really tried, they did exactly what we asked. And here we are, reducing all their work to a single record or filtering out most of their observations.”
These feelings of guilt highlight the Center’s challenge: to pursue data validity while also fostering public involvement. This challenge is directly related to what sociologist Thomas Gieryn called boundary work—the processes by which scientists and institutions define and manage the boundaries between what is scientific and what is not. In citizen science, this boundary work is especially complex, as the line between scientists and participants, between academia and the public, must be flexible enough to allow meaningful involvement, yet rigid enough to uphold scientific standards.

The Center’s staff describe their ability to switch between roles as “wearing different hats.” As scientists, they demand precision and rigor. As educators, they promote enthusiasm and engagement. As one researcher described:
“They’re totally different worlds. It’s a lot more fun to pet furry animals and go on pleasant walks… but that’s problematic data for research. I handle that with my educational hat, not my scientific one.”

This role-switching can be understood as a kind of fractal bio-power structure—a concept I developed inspired by Michel Foucault’s theory of bio-power—how states and institutions regulate populations through knowledge. A fractal is a pattern that repeats at different scales. In citizen science, a unique structure emerges where each actor is simultaneously an observer and the observed, a collector of data and a subject of data collection:

• Participants observe nature and collect data about it—exerting bio-power over the natural world.
• Researchers observe participants, analyze their data, and study their patterns of participation—exerting bio-power over participants.
• The scientific community and the public observe the researchers and their findings—exerting bio-power over the researchers.

Unlike a simple top-down hierarchy, this fractal structure repeats the same pattern—monitoring, data collection, and knowledge production—at every level. Each level watches the one below it while being watched by the one above. This structure allows every actor to feel like a meaningful part of the system and creates a sense of shared ownership, despite differences in roles or expertise. It’s also the key to citizen science’s success in creating win-win projects, where everyone benefits. As one researcher described it:
“The best thing is for each person to bring their own expertise and contribution… through lots of conversations, we find our points of intersection… get to know each other, talk a lot, find common ground, define boundaries.”

The fractal bio-power structure enables citizen science to resolve the paradox at its core: the simultaneous aspiration to be both scientifically rigorous and socially inclusive. Through this structure, each actor can play a unique role that serves the broader system, while also feeling valued and meaningful. When participants see themselves as active researchers (of the natural world), and scientists remain open to being researched themselves (by the community and the public), a new kind of balance emerges — one that makes collaboration possible despite disparities in knowledge and power.

Data Sharing: FAIRness and Equity

The third aspiration of the Center relates to how data is managed and shared. The Center is committed to the FAIR principles, which require data to be:
Findable, Accessible, Interoperable, and Reusable. These principles serve a dual purpose: they allow researchers worldwide to use data efficiently, and they provide transparency to participants, who can see how the data they collected is being used by the scientific community.

However, data sharing raises complex questions about ownership. As one researcher put it:
“I’m not willing to invest all my time for someone else to take this data, analyze it, and publish papers instead of me.”
Another researcher countered:
“You live off public money; you get a salary and grants from public funds. The research must, in some way, return to the public—not just through a lecture you give.”

What’s especially interesting is how FAIR principles reverse the usual dynamic: while scientists typically monitor participants, transparency and accessibility allow participants to monitor scientists, too. They can track how their data is used, creating a reciprocal system of oversight and reshaping the traditional bio-power dynamic in science.

Beyond the Dichotomy

Paradoxically, the attempt to bridge the worlds of science and society requires citizen science to also reinforce the distinction between them. To be “scientific enough,” citizen science must emphasize the difference between scientific and social knowledge; to be “social enough,” it must highlight the distinction between social and scientific activities.

Contrary to romantic or hyper-critical views—which either celebrate citizen science’s success or critique its failures in bridging science and society—I argue that citizen science should not be seen as a monolithic institution, but as a continuous process of negotiation between conflicting aspirations. Its success lies not in achieving one goal at the expense of the others, but in managing the tension among them—at different levels and with complexity specific to each project.

In practice, citizen science emerges as a dynamic and complex entity that thrives precisely within its internal tensions—between its various goals and the real-world need to navigate them. Its strength lies in its ability to develop practices that are simultaneously scientific, social, open, and accessible—without sacrificing any of those dimensions.

Eran Schwartzfuchs is a PhD student in anthropology and holds master’s degrees in ecology and anthropology. This article is based on his master’s thesis, written under the supervision of Professor Michal Kravel-Tovi in the Department of Sociology and Anthropology at Tel Aviv University.