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Imagine walking through a British woodland with your smartphone: you photograph a rare butterfly, record a bird's song, send the data to a national project. You're a citizen scientist β€” one of those volunteers who, without a lab coat, help science monitor biodiversity. In the UK this tradition is old and deeply rooted: over 165 active projects exist, run mainly by large organisations like the Royal Society for the Protection of Birds or the Biological Records Centre. Most follow a "contributory" model: volunteers collect data, scientists analyse it.

But who really keeps these projects running? A recent study interviewed 14 women active in UK biological recording. Their contribution goes far beyond data collection: they mentor newcomers, coordinate volunteer groups, organise outreach events, manage databases, resolve conflicts. It's relational and organisational labour, often invisible in official reports and formal recognition systems.

Interviewees describe expertise built "on the ground", moving across projects, learning independently or swapping tips in informal chats. Access to specialist roles β€” like record verification or training β€” depends on organisational cultures, digital tools, and life circumstances (work, caregiving, health).

Researchers propose a new conceptual framework: "gendered citizen science infrastructures". The idea is that institutions, technologies, and social norms intertwine to create different pathways for men and women. Recognising invisible labour β€” not just data, but community care β€” is the first step toward more inclusive, lasting citizen science. Because when the person holding the project together leaves, the data stops coming.

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The study "Gendered citizen science infrastructures: participation, recognition, and ecological labour in UK biodiversity monitoring" employs a sequential mixed-methods design. In the quantitative phase, the authors conducted a systematic scoping of 165 active UK ecological citizen science projects, identified via institutional directories (NBN Atlas, UKEOF, environmental NGO websites) and validated through inclusion criteria: biodiversity focus, data collection/verification activity, operational as of 2023. Projects were coded for: participation model (contributory, collaborative, co-created per Shirk et al. 2012), target taxa, ecosystem, promoting organisation, formal recognition mechanisms (badges, co-authorship, certificates).

Key scoping results: 78% of projects use a contributory model; strong concentration on "charismatic" taxa (birds 34%, butterflies 22%, vascular plants 18%) and terrestrial ecosystems (89%); formal recognition limited to data outputs (e.g., name in dataset) rather than processes.

Qualitative phase: 14 semi-structured interviews (60-90 min) with women having β‰₯3 years' experience in biological recording, recruited via snowball sampling from volunteer networks (BSBI, Butterfly Conservation, local groups). Reflexive thematic analysis (Braun & Clarke 2006) with inductive and deductive coding (Fraser's 2003 recognition justice framework: redistributive, representative, recognition).

Four emergent themes: (1) "Extended ecological labour": participants describe infrastructural care work β€” onboarding, conflict mediation, digital tool maintenance, funding advocacy β€” unaccounted for in project metrics. (2) Informal expertise pathways: situated learning (Lave & Wenger 1991) through cross-project "communities of practice"; barriers to verification roles (e.g., iRecord verifiers) due to lack of formal pathways. (3) Organisational cultures: "heroic" cultures valuing the solitary field expert vs. collective coordination work; digital platforms designed for data input not community management. (4) Life-course intersections: caregiving, precarious work, health modulate participation intensity and mode; flexibility needed but rarely institutionally supported.

The proposed framework β€” "gendered citizen science infrastructures" β€” connects three levels: macro (policy, funding, governance), meso (organisations, platforms, community norms), micro (individual pathways, identities, relationships). It shows how misrecognition processes (not seeing relational work as "scientific") produce friction, early exit, loss of tacit knowledge. Recommendations: multidimensional recognition metrics (data + care + leadership), transparent progression pathways, participatory platform design, resources for community facilitators.

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Authors:
Artemis SkarlatidouJakleen Al-Dalal'aValentina Chang
Date Published:
Original Article www.frontiersin.org
Fact-check verified on 18/10/2026
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Creative Commons License CC BY-NC-SA 4.0

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