The problem with delineating narrow criteria for citizen science

Auerbach, Jeremy; Barthelmess, Erika L.; Cavalier, Darlene; Cooper, Caren B.; Fenyk, Heather; Haklay, Muki; Hulbert, Joseph M.; Kyba, Christopher C. M.; Larson, Lincoln R.; Lewandowski, Eva; Shanley, Lea

doi:10.1073/pnas.1909278116

Cited by 46 publications

(34 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Heigl et al (2019) argue that defining minimum quality criteria for CS (mode 2) are essential for making CS a more acceptable source of data for decision-making (mode 1) as well as a more legitimate recipient of funding through grants (mode 3). In contrast, the response by Auerbach et al (2019) can be read as a claim not to delineate the methodological approaches and research designs that shall be considered part of CS (mode 2) based on criteria for showcasing CS projects on online platforms for attracting attention and participants (mode 4). The coexistence of different governance modes as well as their relations require more attention; however, they are usually hidden by the discussion of CS as a uniform concept (cf.…”

Section: Discussionmentioning

confidence: 99%

How Does Citizen Science “Do” Governance? Reflections from the DITOs Project

Göbel¹,

Nold

Berditchevskaia³

et al. 2019

Citizen Science: Theory and Practice

Self Cite

View full text Add to dashboard Cite

Introduction Citizen Science (CS) is intertwined with public policy in multiple ways, and the question of how CS can be a resource for decision-making is increasingly debated among those who organise projects as well as among politicians. Around the world, CS is considered relevant at various levels of governance from multilateral programmes, such as the United Nations Environment Programme, to supra-national institutions (the European Union) and individual member states exploring the value of CS for environmental reporting, education, and decision-making (POST 2014; Science Communication Unit 2013). Environmental protection agencies are recognising CS by issuing recommendations, cost-benefit analyses, and decision support for when to use CS to support implementation of regulatory environmental policy (NACEPT 2016; Pocock et al. 2014; Vohland et al. 2016). Increasing openness towards CS is also spreading beyond environmental and biodiversity monitoring to include health and food security, disaster response, and research policy (Schade et al. 2017). Policy publications that mention CS typically highlight its potential for multiple fields and implementations (McElfish, Pendergrass, and Fox 2016) as well as data management (Schade and Tsinaraki 2016). State-sponsored capacity building projects or consultations to develop national strategies for CS have been run in Germany (Bonn et al. 2016), France (Houllier and Merilhou-Goudard 2016), and Spain (Fundacion Ibercivis 2017). Finally, the CS practitioner community is starting to connect with decision makers to demonstrate the validity and benefits of CS (Hecker et al. 2018). Existing studies focus on how CS supports policy development, barriers, and regulatory support (e.g., Chapman and Hodges 2016). Haklay (2015) points out that policy dimensions of CS arise from geography, policy application area, and type of engagement. Existing empirical studies analyse CS projects along dimensions such as standards (Ottinger 2010), place (Newman et al. 2017), participation of stakeholders (Gobel, Martin, and Ramirez-Andreotta 2017), and data practices (Gabrys, Pritchard, and Barratt 2016). The literature typically identifies two roles for CS in policy contexts: As a data source for the development, implementation, or monitoring of regulation and as one of the targets for science policy. This idea rests on a set of basic assumptions: Politicians and formal political institutions are considered as central actors. "Science" and "politics" are understood as separate spheres, and policymaking is seen as a linear process where policy makers determine rules that CS feeds into.

show abstract

Section: Discussionmentioning

confidence: 99%

How Does Citizen Science “Do” Governance? Reflections from the DITOs Project

Göbel¹,

Nold

Berditchevskaia³

et al. 2019

Citizen Science: Theory and Practice

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are different terms (e.g. community science, crowd science, crowd-sourced science) and different understandings when referring to citizen science (Riesch and Potter 2014 ; Auerbach et al 2019 ). Here we take a broad view of citizen science as the “involvement of volunteers in research” (Dickinson et al 2010 ) and consider it a “tool” for undertaking research and monitoring, while also engaging with many people” (Pocock et al 2014 ), however we make the distinction from general surveillance as citizen science programmes have specific hypotheses, goals or target species.…”

Section: The Nature Of Passive Surveillancementioning

confidence: 99%

The role of passive surveillance and citizen science in plant health

et al. 2020

View full text Add to dashboard Cite

The early detection of plant pests and diseases is vital to the success of any eradication or control programme, but the resources for surveillance are often limited. Plant health authorities can however make use of observations from individuals and stakeholder groups who are monitoring for signs of ill health. Volunteered data is most often discussed in relation to citizen science groups, however these groups are only part of a wider network of professional agents, land-users and owners who can all contribute to significantly increase surveillance efforts through “passive surveillance”. These ad-hoc reports represent chance observations by individuals who may not necessarily be looking for signs of pests and diseases when they are discovered. Passive surveillance contributes vital observations in support of national and international surveillance programs, detecting potentially unknown issues in the wider landscape, beyond points of entry and the plant trade. This review sets out to describe various forms of passive surveillance, identify analytical methods that can be applied to these “messy” unstructured data, and indicate how new programs can be established and maintained. Case studies discuss two tree health projects from Great Britain (TreeAlert and Observatree) to illustrate the challenges and successes of existing passive surveillance programmes. When analysing passive surveillance reports it is important to understand the observers’ probability to detect and report each plant health issue, which will vary depending on how distinctive the symptoms are and the experience of the observer. It is also vital to assess how representative the reports are and whether they occur more frequently in certain locations. Methods are increasingly available to predict species distributions from large datasets, but more work is needed to understand how these apply to rare events such as new introductions. One solution for general surveillance is to develop and maintain a network of tree health volunteers, but this requires a large investment in training, feedback and engagement to maintain motivation. There are already many working examples of passive surveillance programmes and the suite of options to interpret the resulting datasets is growing rapidly.

show abstract

“…There are several efforts to create a definition for citizen science, one of most recent being proposed by five Austrian authors 46 which triggered a flurry of online discussion and resulted in the publication of a response pointing to the problem that we have in delineating narrow criteria for citizen science. 47 We think that certain criteria and tools could be adopted and employed to create a more inviting environment and to provide access and power to those lay people who wish to engage with scientific efforts. At the same time, this approach should offer trustworthy data and further contributions by researchers.…”

Section: Case Study 3: Citizen Sciencementioning

confidence: 99%