Despite the differences, community based monitoring and citizen science share several common characteristics. This blog post departs from this perspective and identifies four theoretical frameworks that underpin both approaches, principal-agent theory, collection action, utilization-focused evaluation, and participatory research frameworks.
Over the past decades, Citizen Science (CS) and Community-Based Monitoring (CBM) have become recognized interventional approaches for better public governance and development mechanisms. They are continuously used as innovative tools/models for enhancing citizen engagement in governance and research (Rask et al., 2918). CS refers to the active involvement of citizens in scientific research. Researchers (scientists) collaborate with citizens to produce knowledge that can be used for scientific purposes as well as community development (Vohland et al., 2021). Researchers team up with groups of citizen volunteers to collect data to monitor and manage public goods (Conrad & Hilchey, 2011). The same collaborative mechanisms used in CS are used in CBM initiatives. CBM, also known as community-led monitoring (CLM), involves a combination of mechanisms used by users of public services or resources to generate information that can be used to assess intervention processes and outcomes with the aim of learning to improve service delivery, resource management and accountability (Björkman & Svensson, 2009; Waddington et al., 2019; The Global Fund, 2020). Community members (citizens) are involved in a systematic process of “measuring, recording, collecting, and analyzing information and communicating and acting on that information” to improve efficacy and effectiveness (Grandvoinnet et al., 2015, p. 295). In other words, CS and CBM as learning and social accountability mechanisms or tools.
While CS and CBM are treated differently, they are closely related. There are several overlaps between the goals and characteristics of CS and CMB initiatives. Because of such overlaps, Tiholaz (2022a) attempts to relate the two as identical twins or siblings. The main similarity is in the benefits that citizens gain skills, citizen empowerment and social capital development, and trust building between actors involved (Tiholaz, 2022a). The differences relate to how citizens are engaged (e.g., voluntary vs mandatory), coverage of interventions/initiatives (local vs national vs global), who is more involved with citizens (e.g., researchers vs government), and the main purpose (monitoring government vs knowledge production or learning vs accountability or both) (Tiholaz, 2022a; Conrad & Hilchey, 2011; Khair et al., 2021). In general, CBM is treated as a subset of CS. While CS is more focused on learning and feedback, CBM, as part of CS, adds the accountability function. This blog post departs from the identified similarity between these two approach to identify four theoretical perspectives that underpin them, principal-agent theory, collection action perspective, utilization-focused evaluation, and participatory research perspective. Each of them is briefly discussed below.
Principal-Agent theory
The Principal-Agent theoretical framework stipulates that the principal contracts the agent to perform on the principal’s behalf and therefore the agent is liable to be monitored to avoid performance problems and align the principal’s interests with the agent’s interests (Gauld, 2016; Steele & Scherrer, 2018). The principals may include government ministries and agencies, donors, and users of public goods and services [citizens], while the agents include providers of goods and services (Ebrahim, 2003; Brinkerhoff & Bossert, 2014). Public officials cannot be trusted therefore have to be scrutinized for discipline and performance improvement (Grandvoinnet et al., 2015). This Principal-Agent theory/model helps in understanding the functioning of CS and CBM initiatives while focusing on citizens/community members (part of principals) in researching and monitoring providers of public goods and services (agents). From a social accountability perspective, citizens provide information that can be used as a tool for accountability. With more involvement in the information flow network, citizens have more information at their disposal that they can use to demand rights, entitlements, performance, and opportunities (Grandvoinnet et al., 2015). Social accountability thus requires active participation. Participant, or otherwise citizen, engagement depends on the level of citizens’ empowerment, choices, and voices (IRC, 2015). The engagement of citizens may be direct or indirect using confrontational, collaborative, or consensus mechanisms (Grandvoinnet et al., 2015; World Bank, 2005; Brummel, 2021). However, any strategy the citizens choose to take is normally supported by other actors, including Civil Society Organizations (CSOs), media, academia, etc.
Collective action perspective
Collective action is what service users do in (a) group(s) through coordinated mechanisms with a common goal to improve conditions (Olson, 1965). Collaborative actions, whether through CBM or CS initiatives, can ease information flow between information suppliers and demanders, thus facilitating empowerment and meaningful engagement (Conrad & Hilchey, 2011). However, collective action depends on the agency and social actors' knowledge (Emirbayer & Mische, 1998; Narayan & Petesch, 2005). The effectiveness of collective action in CBM/CS initiatives can be limited if citizens as significant actors do not have the agency, social capital, and perceived efficacy for collective action (Dewachter & Holvoet, 2017). However, as Olson (1965) asserts, there is a likelihood that even if some CBM mechanisms provide opportunities for citizen participation, some citizens may not want to participate in collective actions (the free-rider problem). This also relates to the rational choice theory of collective action. The rational choice theory explains that individuals base their self-interests to choose to be part of collective efforts or not (Ostrom, 1998).
Utilization-focused evaluation framework
Utilization-focused evaluation (UFE) is based on the principle that regardless of how information has been gathered and produced, if the information is not used, the process is regarded as useless (Patton, 2008). The framework contends that all potential users should participate in information gathering, analysis, and usage.
Like the general monitoring and evaluation practices, learning and accountability are fundamental aspects of CS or CBM initiatives. The CS and CBM process involves data collection and analysis of information later used for learning and accountability purposes (Ringold et al., 2012). From a theoretical perspective, the utilization-focused evaluation (UFE) approach is thus essential in guiding the use of information from CS and CBM initiatives. Citizens can use the information to hold service providers and decision-makers accountable, but also, decision-makers can use the information to hold service providers accountable (downward vs upward accountability) (Grandvoinnet et al., 2015). Several stakeholders or actors including, the state, CSO, media, researchers/academia, and citizens can use information in CS and CBM. Limited information generation and dissemination may affect initiatives' effectiveness and limit citizens' participation (Molina et al., 2017).
Participatory perspective in CS and CBM
From a “voice and participation model” stance, CS and CBM initiatives are defined by how and why citizens participate (Grandvoinnet et al. (2015). CS and CBM initiatives are recognized as strategies for empowering people, giving them power and voice, and promoting transparency and accountability (PEPFAR, 2020; Björkman & Svensson, 2009; Waddington et al., 2019; Molina et al., 2017; Baptiste et al., 2020). However, the effective realization of such benefits is dependent on the level of citizen participation. Participation is a process of engaging citizens in development and research processes to collectively work with other actors to conduct their own assessments and work towards reducing everyday challenges and making transformational changes. Cornwall & Jewkes (1995) refer to participation as a transformative mechanism for development and research because it allows citizens to assess realities and act to make improvements. Participation can take many forms based on the objectives of the initiative and the relations between those involved. See Arnstein (1969), Chambers (1995), Pretty (1995), and Cornwall (2008) for typologies and levels f citizen participation. For Grandvoinnet et al. (2015), the citizens can participate as decision makers, service provisioning as contractors, management, oversight committee members, and planning and budgeting.
In CS initiatives, citizens participate in conducting scientific research to be part of knowledge production and feedback provision for learning. In other words, it is participatory research (Cornwall & Jewkes, 1995). In CBM initiatives, citizens can participate in collecting data, analyzing it, and using it to actively and collectively demand improved public goods and services. Thus, CBM should not be treated as a mere tool for improving performance but also as a strategy for improving citizen participation by rendering them voice and power (Tiholaz, 2022b; Grandvoinnet et al., 2015). However, citizen participation is not a straightforward process. Participation of citizens in CS and CBM initiatives can be limited by the low level of empowerment, low education, individual perceptions on the efficacy of initiatives, cultural and political structures, limited information on how to participate, high participation opportunity cost, the divergence of beliefs among actors, etc. (Dewachter & Holvoet, 2017; Howard et al., 2018; Molina et al., 2017; Conrad & Hilchey, 2011).
References
Arnstein, S. R. (1969). A ladder of citizen participation. Journal of the American Institute of Planners, 35(4), 216-224.
Baptiste, S., Manouan, A., Garcia, P., Etya’ale, H., Swan, T., & Jallow, W. (2020). Community-Led Monitoring: When Community Data Drives Implementation Strategies. Curr HIV/AIDS Rep, 17(5), 415–421.
Björkman, M., & Svensson, J. (2009). Power to the people: Evidence from a randomized field experiment of a community-based monitoring project in Uganda. Quarterly Journal of Economics, 124(2), 735–769.
Brinkerhoff, D. W., & Bossert, T. J. (2014). Health governance: principal-agent linkages and health system strengthening. Health policy and planning, 29(6), 685–693.
Brummel, L. (2021). Social Accountability Between Consensus and Confrontation: Developing a Theoretical Framework for Societal Accountability Relationships of Public Sector Organizations. Administration & Society, 53(7), 1046–1077.
Chambers, R. (1995). Poverty and livelihoods: whose reality counts? Environment and Urbanization, 7(1), 173-204.
Conrad, C., & Hilchey, K.G. (2011). A review of citizen science and community-based environmental monitoring: issues and opportunities. Environmental Monitoring and Assessment, 176, 273-291.
Cornwall, A. (2008). Unpacking ‘Participation’: models, meanings and practices. Community Development Journal, 43 (3), 269-283.
Cornwall, A., & Jewkes, R. (1995). What is participatory research? Soc. Sci. Med. 41(12), 1667-1676.
Dewachter, S., & Holvoet, N. (2017). Intersecting social-capital and perceived-efficacy perspectives to explain underperformance in community-based monitoring. Evaluation, 23, 339–357.
Dewachter, S., & Holvoet, N. (2017). Intersecting social-capital and perceived-efficacy perspectives to explain underperformance in community-based monitoring. Evaluation, 23, 339–357.
Ebrahim, A. (2003). Accountability in practice: Mechanisms for NGOs. World Development, 31(5), 813–829
Emirbayer, M., & Mische, A. (1998). What is Agency? American Journal of Sociology, 103(4), 962-1023.
Gauld, R. (2016). Principal-Agent Theory of Organizations. In: Farazmand, A. (eds) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham
Grandvoinnet, H., Aslam, G., & Raha, S. (2015). Opening the Black Box: The Contextual Drivers of Social Accountability. The World Bank.
Hecker, S., Haklay, M., Bowser, A., Makuch, Z., Vogel, J., & Bonn, A. (Eds.). (2018). Citizen Science: Innovation in Open Science, Society and Policy. UCL Press.
Howard. J., López Franco, E., & Shaw, J. (2018). Navigating the pathways from exclusion to accountability: from understanding intersecting inequalities to building accountable relationships. Institute of Development Studies
IRC. (2015). Social Accountability: An Introduction to Civic Engagement for Improved Service Delivery. International Rescue Committee
Khair, N. K. M., Lee, K. E., & Mokhtar, M. (2021). Community-based monitoring for environmental sustainability: A review of characteristics and the synthesis of criteria. Journal of Environmental Management, 289 (112491), 1-13.
Molina, E., Carella, L., Pacheco, A., Cruces, G., & Gasparini, L. (2017). Community monitoring interventions to curb corruption and increase access and quality in service delivery: a systematic review. Journal of Development Effectiveness, 9(4), 462-499.
Narayan, D., & Petesch, P. (2005). Agency, Opportunity Structure and Poverty Escapes. In D. Narayan & P. Petesch (Eds.), Moving Out of Poverty. Cross- Disciplinary Perspectives on Mobility (pp. 1-44). Washington D.C.: World Bank.
Olson, M. (1965). The Logic of Collective Action: Public Goods and the Theory of Groups. Cambridge, MA: Harvard University Press.
Ostrom, E. (1998). A Behavioral Approach to the Rational Choice Theory of Collective Action: Presidential Address, American Political Science Association, 1997. American Political Science Review, 92(1), 1-22.
Patton, M.Q. (2008). Utilization-focused evaluation, 4th edition. Sage Publications.
PEPFAR. (2020). The People’s Voice – Community Priorities COP20 – Uganda. Washington, DC: President's Emergency Plan For AIDS Relief (PEPFAR).
Pretty, J. (1995). Participatory Learning for Sustainable Agriculture. World Development, 23(8), 1247–1263.
Rask, M., Mačiukaitė-Žvinienė, S., Tauginienė, L., Dikčius, V., Matschoss, K., Aarrevaara, T., & D'Andrea, L. (2019). Public Participation, Science and Society: Tools for Dynamic and Responsible Governance of Research and Innovation.
Ringold, D., Holla, A., Koziol, M., & Srinivasan, S. (2012). "Citizens and Service Delivery: Assessing the Use of Social Accountability Approaches in the Human Development Sectors," World Bank Publications
Steele, J., & Scherrer, P. (2018) Flipping the principal-agent model to foster host community participation in monitoring and evaluation of volunteer tourism programmes, Tourism Recreation Research, 43:3, 321-334
The Global Fund. (2020). Community-based monitoring: An Overview. Geneva: The Global Fund.
Tiholaz, D. (2022a). Citizen Science and Community-based Monitoring: Identical twins, siblings or …? Communitor. https://commun1tor.wixsite.com/my-site/post/citizen-science-community-monitoring
Tiholaz, D. (2022b). Community Based Monitoring Theory: Guiding and blinding. Communitor. https://commun1tor.wixsite.com/my-site/post/community-based-monitoring-theory-guiding-and-blinding
Vohland, K., Land-zandstra, A., Ceccaroni, L., Lemmens, R., Perelló, J., Ponti, M., Samson, R., & Wagenknecht, K. (2021). The science of citizen science. 1st ed. Cham: Springer International Publishing
Waddington et al., (2019) Citizen engagement in public services in low‐ and middle‐income countries: A mixed‐methods systematic review of participation, inclusion, transparency and accountability (PITA) initiatives. Campbell Systematic Reviews, 15(1-2), 1-90.
World Bank. (2005). Social Accountability in the Public Sector: A Conceptual Discussion and Learning Module. World Bank
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