Many initiatives for impactful collaboration for the common good are centrally controlled, think UN and national government programs or classic corporate social responsibility initiatives funded by large corporations. These top-down initiatives are important, yet only effective up to a point. Combating wicked problems like climate change requires a mass mobilization for social innovation. This implies that a fundamental role is played by collaborating communities that continually make connections between seemingly disparate public initiatives [1
It is not sufficient for this multitude of societal change stakeholders to work on their own. Working effectively for the common good requires achieving collective impact: the commitment of important actors from many different sectors to a common agenda for solving a specific social problem [3
]. In true cross-sector collaborations, no single actor or agency monopolizes the power to set goals, shape agendas, and determine key policies and practices [4
]. Keys to successful alignment of collective impact initiatives are that stakeholders should not (just) focus on their individual organizational goals, but on the joint outcomes they want to achieve. This includes drawing a picture big enough to see how and why existing initiatives can connect [5
Aligning such a wide array of efforts toward collective impact can be supported by collective intelligence approaches for the common good (CI4CG), especially when boosted by the Internet. CI4CG approaches include—but are not limited to—online deliberation, sensemaking, argumentation and discussion-mapping, community ideation and idea management systems, collective decision making, group memory, participatory sensor networks, early warning systems, collective awareness, and crowdsourcing [6
]. However, CI4CG methods and techniques by themselves are not enough. It is essential to socio-technically embed them in community networks.
Society is really a community—or rather a network—of communities [7
]. Communities are groups of people sharing social ties and interactions for mutual benefit—which can be a shared purpose, interest, or need—in a common space [8
]. Community informatics (CI) is the application of information and communications technology (ICT) to enable and empower community processes [11
]. As a field of inquiry and practice, it stresses the ethics, empowerment, and legitimacy of socio-technical interventions in, for, and by communities. One angle for applying these insights is building—or more precisely, growing—individual communities, such as cultivating specific communities of practice [12
], communities of interest, online communities, and so on. However, the field of community informatics also fits the collective intelligence paradigm. It goes beyond building individual communities, looking at the societal context in which multiple communities are embedded. It provides an alternative way of thinking and working that promotes effective participation from the bottom-up by local communities in regional, national and even global decision making processes [13
]. The metaphor to be used here is that of “networked societies” rather than “information societies”, where the network, networked relationships, and those relationships being ICT-mediated pervasively transforms all aspects of these network-based transactions and interactions [11
] (pp. 15–16). This line of thinking has been solidified in the Community Informatics Declaration, which states that a just and equitable Internet provides recognition that the local is a fundamental building block of all information and communications and the “global” is a “federation of locals” [14
Still, what does it mean to become such a federation? What does it mean to connect communities into federated networks that can achieve impact on wicked problems? Graham sees the distributed governance structures and processes of community networks as scaling fractally, society being a fractal composite of communities. Communities, in his view, are complex adaptive systems, adjusting situational individual responses to emergent experiences, such that the system stays in balance with the context that defines it. This requires a governance that occurs through self-organization rather than (top-down) control, and a relational—focusing on the network—epistemology rather than a mechanistic—top-down control—one [15
]. There is some evidence that this emergent inter-communal collaboration does indeed occur. An example are forms of open-cooperativism where various communities work on their joint commons [16
]. Still, this organic process is haphazard at best. Unclear is how to more systematically boost such a self-organization process of a ‘federation of locals’ grounded in such a ‘relational epistemology’ and ‘scaling fractally’. How do we begin to realize these necessary but abstract goals at scale? This is where the literature gets fuzzy and few operational methods are offered.
Starting from this point of view of society consisting of federated, organically evolving communities, we need to clarify the concept of community network. Traditionally, a community network referred to the technical communications infrastructure of a community (e.g., [17
]). However, from our perspective, we are more interested in the socio-technical interpretation of the term. The boundary between communities and social networks is a fuzzy one, them being part of a continuum. The network aspects refer to the relationships, personal interactions, and connections among participants, providing affordances for learning and collaboration; the community aspect refers to the development of a shared identity around a topic or set of challenges [18
]. Following this interpretation, we see individual communities as being embedded in a much larger socio-technical network context of a commons of partially overlapping actors, goals, interactions, resources, tools, and so on. Vice versa, this socio-technical context is a medium, a substrate with the potential for forming new connections between multiple communities. One example is a topic of interest they may have in common, a person or an organization being a member of multiple communities, social media that act as conversational bridges between communities, a physical meeting space in which multiple communities convene, and so on. To be precise about the object of our analysis, we define a community network as a network of multiple communities, each with its individual socio-technical context, as well as the common socio-technical context in which those communities are embedded and linked.
As we saw, communities have traditionally been defined as striving towards the mutual benefit of their own members. Communities work because their members are attached to them for two reasons: identity-based attachment—they like the group as a whole—and bond-based attachment—they like individuals within the group [10
]. When opening up the definition of community in terms of community networks, with their broader, overlapping contexts, what is that mutual benefit? Of course, the communities making up the network focus on their own purposes, interests, and needs first. Still, through their intersecting socio-technical contexts, those purposes, interests, and needs partially connect the communities. This means that larger, overarching, common good constructs may become focal points of interest around which inter-communal joint purposes, interests, and needs can emerge, be more explicitly defined, linked more closely, and strengthened. The question is: how? Given that communities all have their own interests, characteristics, culture, and language, how to make sense across their boundaries in order to explore and expand their common ground? How can they do so to scale up their collaboration for collective impact? Collaborative sensemaking is at the heart of the process of communities scaling up smartly. In this process, stakeholders use new understandings, processes, and tools to collaborate in complex thinking and decision-making processes [19
]. To get to impact at the societal (network of communities) level, inter-communal sensemaking is of the essence, involving an ongoing, complex process of aligning resources, practices, and initiatives of multiple communities [20
]. A powerful technique for making common sense is to help stakeholders map their own collaboration ecosystem: visualizing the—to themselves—network of most relevant themes and issues at play, stakeholders involved and their interrelationships, the processes in which they collaborate, the resources and tools that they use, and so on. By then jointly making sense of such visualizations through discussions in, say, brainstorming workshops or one-on-one conversations, it becomes much easier to see what are the collaborative strengths and weaknesses, opportunities, and threats. Their maps can help set the agenda for productive conversations on the community current state of affairs and directions it should grow into, as well as capture and preserve the essential outcomes of such conversations. Used in such a way, a map can act as a device for achieving common understanding and emergent coordination, while still allowing for multiple interpretations.
The participatory collaboration mapping approach presented here extends work on the CommunitySensor methodology for participatory community network mapping [20
]. We defined this as the participatory process of capturing, visualizing, and analyzing community network relationships and interactions and applying the resulting insights for community sensemaking, building, and evaluation purposes [21
]. CommunitySensor uses a cyclical approach by adopting a community network development cycle that embeds a community network sensemaking cycle. In the development cycle, community members first map relevant—to them—parts of their community networks. In the sensemaking process, relevant stakeholders subsequently reflect upon and discuss those partial maps, reaching consensus on focal issues, priorities, and next actions. This mapping and sensemaking process often needs to be reiterated several times in the sensemaking cycle. This helps weaving partial maps together, while further clarifying their meaning. The issues, priorities, and next actions that are arrived at in the sensemaking stage form inputs for their community network building interventions. In the final stage of that process, community members evaluate the results of those interventions, in turn capturing these results on the map.
In the current article, we shift our attention from the collaboration practices involved in individual communities making sense of themselves and their own socio-technical context to multiple communities making joint sense on their way to collective impact. We specifically look at how to support collaborative sensemaking within and across communities and their surrounding socio-technical networks [22
]. One complicating issue when trying to make sense across multiple communities is that not only do different communities have different cultures and practices, but also different epistemologies: different languages to describe their community and the soci(et)al context it operates in, with often different meanings attached to the terminologies used. However, when communities need to work together—in order to achieve collective impact—somehow these different epistemologies need to be resolved. On the one hand, each community needs to be able to keep using its own words to describe the world according to its unique perspective, yet on the other hand, some common epistemological ground needs to be found to know how the terms and underlying concepts of the various collaborating communities might interrelate. Of course, any such potential conceptual interrelationships are hypotheses only, and need to be interpreted and agreed upon in extensive inter-communal sensemaking conversations. Still, without some minimal conceptual common ground to begin with to make sense across community boundaries, inter-communal collaboration is bound to lead to even more misunderstandings about meanings. But then, what could be such a conceptual common ground that is broad enough to stretch across community boundaries, specific enough to be useful to support collaboration towards collective impact, and customizable enough to be adapted to a community’s specific terminological needs?
An ontology is an explicit specification of a conceptualization. Ontologies have many purposes, for example allowing for more systematically building knowledge bases and enabling knowledge management practices and processes [23
]. Ontologies are inseparable from the communities in which they are being created and used [25
]. Drawing from the existing theoretical work on building ontologies in information science, we propose the conceptual foundation for a relational epistemology—a tentative community network ontology—that can be used to help community networks define their own, tailored mapping languages. The contents of this ontology is grounded in a growing body of work of mapping-good practices, produced by analyzing an increasing set of real-world cases. Based on the ontology, mapping languages can be tailored to the specific mapping needs of a community network. Using a customized mapping language, the maps of their collaboration ecosystems can be made more relevant, provided the mapping and sensemaking process also fits the community network, which as we have stated above, we have paid attention to in earlier work. Making sense of more relevant maps, community networks can scale up their collaboration with more efficacy, creating stronger federations of communities working together. To clarify, our approach can also be used by individual communities to map their own collaboration ecosystem. Still, the methodology is specifically aimed at community networks of multiple communities with at least partially overlapping common ground, since in that case, conceptual confusion tends to be even larger than in communities with a more stabilized culture, language, and interactions.
Creating a community network ontology is therefore about much more than just knowledge representation. It also requires us to think about how this conceptual knowledge model affects real-world knowledge creation and application processes, in our case concerning participatory community network mapping. Its participatory nature means that we need to think hard about how to explicitly involve the community in the construction, evolution, and use of the ontology. We thus need a richer perspective than in much of the classical IS development literature. That body of knowledge often does not sufficiently take into account the human-centered aspects that predominate in community informatics, like ethics, legitimacy, empowerment, and socio-technical design. Particularly in knowledge engineering and IS design, systems specification is often driven by abstract conceptual frameworks and methodologies, not sensitive at all to the subtle knowledge constructs, modeling languages, elicitation, and validation processes that are of the essence in community networks. Ontologies are at the heart of such traditional systems designs. Exactly by focusing in this article on these ontologies, but from a community informatics perspective, we hope to shift some of the thinking of traditional knowledge engineers. Vice versa, many researchers and practitioners who are mainly interested in human-centered social constructs choose to ignore the to them often alienating world of technical systems design. This is also not the right way to go. The whole world is increasingly running on networked ICTs, big data, algorithms, and social networks like Facebook, with all of the ensuing contentious issues like loss of privacy and lack of self-determination. We can wish that technical world away, but that only increases the colonization of ordinary people’s lifeworlds by those systems. The unique strength of community informatics as a field is that it aims to build bridges across the human-centered and information systems paradigms. Difficult as it is, those working from either paradigm need to find ways to make inroads into each other’s domains. Community network ontologies, their creation, and use in participatory collaboration mapping are one way to bridge those two worlds and help build socio-technical systems with collective impact that are grounded in more emancipatory norms and values. This article, although only in a tentative way, hopes to make a contribution to that goal.
To show how community network ontologies driving participatory collaboration mapping can promote these goals in practice, we make the community network ontology evolution and use come to life by sharing a typical real-world example: participatory collaboration mapping for international agricultural field building, followed by the outline of a spin-off case on national agricultural field building currently underway. In the first case, we show how participatory collaboration mapping was used to establish the common ground in the interests and work of participants of a global agricultural conference. The human-centeredness of the methodology showed in how the seed map was constructed out of participants’ descriptions of the context of the projects they came to represent at the conference; how during the conference the participants engaged interactively with the map through a combined process of mapping and facilitation; and how the mapping language grounded in the community network ontology was modified based on inputs from the participants as they engaged with the maps during the conference.
Out of the mapping exercises done at the conference, a spin-off case emerged: participatory mapping of stakeholder collaborations in Malawi. This country has a very complex and hierarchical agricultural governance system, resulting in a multitude of collaboration problems. A participatory collaboration mapping approach is being developed which consists of a common mapping language that is used to map the connections between local collaborations, then aggregate these maps to engage with them at the higher district and even national levels. Much attention is paid in the methodology to map ownership and community empowerment. For example, the approach allows local communities to remain in control of their own maps, while it simultaneously permits collaboration patterns to be spotted at the aggregate levels. This permits, for instance, agencies with resources to contribute to addressing relevant collaboration issues that are made visible by the maps. Both cases are an exemplary illustration of participatory collaboration mapping for collective impact, and the role that community network ontologies play in configuring the right mapping languages.
Important ontology-research questions we aim to at least partially address here include: how can we bridge the conceptual gap that often exists between networks of communities to reach beyond their individual interests and practices? How can we externalize the tacit knowledge of participants in community networks into conceptual structures that provide a means for others to interact with, react to, negotiate around, and build upon [26
]? What is the essential content and structure of a community network ontology? How can we use the ontology to create inter-communal maps? How can an ontology help a network of collaborating communities to scale towards impact? What collaboration patterns grounded in the ontology can be identified to support this scaling up of the collaboration?
We introduce our community network ontology by first presenting our agricultural field building case. We then explore what are ontologies and how our community network ontology is continuously evolving. We present the community network conceptual model underlying the ontology. We show how it contains a classification of element and connection types into a set of categories, derived from an analysis of 17 mapping cases. We show how the community network ontology was developed and used in the agricultural conference case. We end the article with a discussion and conclusions.
2. Participatory Collaboration Mapping for Collective Impact: The INGENAES and SANE Agricultural Field Building Cases
One class of applications of participatory community network mapping is participatory collaboration mapping. One subset of these applications—specifically aiming to achieve collective impact across communities—concerns field building. Field building is about finding “new ways of connecting existing fields and domains to solve increasingly complex problems (https://www.rockefellerfoundation.org/blog/philanthropy-as-field-builder/
). It requires the working together of many stakeholders across sectors and disciplines, with new ways of facilitating knowledge exchange, co-learning, and collaboration, while relationships and forms of collaboration continue to expand and increase in scale [27
]. Field building in particular is an activity in which many different communities—communities of practice, communities of interest, online communities, communities of place, intentional communities, and so on—need to mesh, and where inter-communal sensemaking is needed. In [22
], we showed how we used the CommunitySensor participatory community network mapping methodology together with the online network visualization tool Kumu to support multidisciplinary and international field building in the case of the INGENAES agricultural conference. We summarize the essence of that case in this section and extend it to show what participatory collaboration mapping looks like in practice. In Section 5
, we will zoom in on the how the ontology for this community network was created and used.
We first describe the background of the case, drawing from [28
]. We introduce the online Kumu network visualization tool we used in the mapping. We describe how we created a seed map of the collaboration ecosystem prior to the symposium, and used and expanded it during the conference. We evaluate the impact of the mapping process, ending with an outline of the spin-off case to map agricultural stakeholder collaborations in Malawi currently underway.
Knowledge and learning exchanges as well as network building are key components of the United States Agency for International Development (USAID) funded Integrating Gender and Nutrition within Agricultural Extension Services (INGENAES) project (https://ingenaes.illinois.edu/
). The project aims to stimulate the intersection between the sub-domains of gender, nutrition, and agricultural extension services so that not only are farmers maximizing their participation in the agricultural value chain, but the nutrition needs of themselves, their families and communities are also served with the additional aspect of the pivotal role of women in this field.
The January 2017, INGENAES Global Symposium and Learning Exchange in Lusaka, Zambia, aimed to use participatory collaboration mapping to catalyze this process, connecting practitioners and researchers across the sub-domains of the field, including participants designing and committing to follow-up activities back home. Participants represented many different projects and communities from around the world. The types of communities surrounding the projects they are involved in ranged from local communities, such as village-based projects in West Africa, to large international communities of practice of experts in the various domains of expertise being integrated. INGENAES itself represents a community network of such communities and other stakeholders. As a long-running program, it has been conducting many community-building activities over the years, through its programmatic activities in various countries, its smaller regional conferences and workshops, and, of course, online. The Zambia conference was to be a major community-building “heart-beat” event, along the lines of strengthening the sense of community when cultivating communities of practice [12
]. Typically, such large conferences have only limited time available to do the actual match-making between participants. Many conference activities consist of speeches, lectures, and rather ad hoc group activities. It was felt that using mapping could help in (1) creating more customized conversation agendas for participants so that they knew better whom to talk to at the conference and on what topics, as well as (2) capturing key outcomes from the conference, to continue the conversation after the conference and share the findings with those who could not attend.
The mapping approach used at the conference was three-fold: (1) preparing a pre-conference online map to make sense of what the status was of the collaboration in the projects represented at the conference; (2) using group facilitation techniques to have conference participants generate new insights and make new connections—using the map as a reference and conversation agenda; and (3) visualizing some key lessons learnt and ideas for potential new actions on the map.
2.2. Mapping with the Online Kumu Network Visualization Tool
) is a web-based tool to capture, visualize, and leverage community and network relationships. Kumu maps consist of elements and connections between the elements. Elements can, for instance, be visualized by their own colors, icons and sizes, while connections can be displayed by the combination of color and width of their lines. On the map, one can define different views, in which Kumu only shows those elements and connections of interest in the way desired. Views can be constructed by selecting sub-sets of the elements and connections on the map through focus and filters. Focus allows one to zoom in on the direct context of a selection on the map. Filter is used to select which types of elements and connections should be made visible according to advanced search criteria. The resulting views get their own customized hyperlinks and can be easily shared.
2.3. Prior to the Conference: Preparing the Seed Map
Data for the pre-conference map were collected by sending out a survey to 102 conference registrants, in which participants were asked to describe a key project they represented at the conference. For each project, the respondents were asked to briefly describe their project, key expected project activities/results, key other organizations involved, country of work, estimated number of clients/beneficiaries reached, estimated number of people involved in the project, and—essential—what INGENAES themes the project contributed to. A total of 69 responses were received. In total, 54 ‘Projects/Initiatives’ and ‘Research Projects’ were mapped (some of the responses described the same project and were therefore collated). A spreadsheet was created consisting of all responses, which was the basis for creating the online map using the Kumu network visualization tool. Figure 1
shows a part of the ‘Themes & Projects’-view (https://kumu.io/ingenaes/2017-ingenaes-global-symposium#ingenaes/themes-projects
) on the map.
2.4. During the Conference: Expanding the Map
The mapping-related conference activities alternated between plenary sessions, in which for example the ‘mapping story’ was told and group work sessions in which small groups of participants explored lessons learnt so far and potential for new collaborations. A professional facilitator supported these breakout-group activities. Participants were shown how to use the map by using various forms of online documentation, plenary demonstrations, hands-on support by the facilitator during the group activities, a mapping station where participants could get assistance from the conference mapper, and sharing of links to interesting map views by the social media team on, for example, Twitter.
One way in which the map was used in small-group activities was to quickly get a sense of what INGENAES themes the projects represented by the participants in the group had in common by selecting a ‘thematic view’. To illustrate, say that a breakout-group consisted of participants of the following three projects: SN4A (Sustainable Nutrition for All), Environmental Action (Benin), and Mawa (Zambia).
By selecting these three (orange) projects and then showing their direct context (the directly connected (green) themes in the overall Themes & Projects
-view (Figure 2
), the thematic common ground for these three projects can immediately be seen (see for live view https://bit.ly/2I0uSdX
). The common ground for this example (indicated by the red rectangle in the figure) includes the themes ‘Farmer-to-Farmer Extension’, ‘Utilize Gender Analysis’, ‘Homestead Gardens’, and ‘Highly Nutritious Crops’. Instead of spending precious time finding out what interests the group members have in common, such a map view of their core thematic interests helps them to immediately get to business.
The outcomes of the sensemaking discussions in the break-out groups were first captured on forms by the participants and later added by the mapping team to the Kumu map as ‘Wisdoms’ (insights that participants wanted to share based on their expertise and experience) and ‘Seed Actions’: potential actions a selection of which could be developed further in future work (Figure 3
). The submitted action descriptions were good examples of potential collective impacts as they show seeds for collaboration to be planted and nurtured after the conference.
As this participatory process of mapping and sensemaking continued, the map continued to grow. Whereas the seed map at the beginning of the conference contained 398 elements and 2166 connections, these numbers had grown to 524 elements and 2468 connections after the conference results had been processed.
2.5. After the Conference: Scaling Up towards Collective Impact
], we presented an evaluation of the initial conference impact: “Altogether, 98 seed actions were collected during the conference, each providing the potential for growing into a field building collaboration. From a post-conference survey, filled out by 113 participants, we received promising feedback, showing real interest. 5 people indicated that the mapping approach was an action, tool, method, or approach that emerged for them and which could be integrated in their work (e.g., “I got a peek at many, but now need to go deeper. The Map and links will help”); 6 respondents reported on getting to know the mappers was their key new connection made who could help them with their work (“connection on mapping to connect volunteers in their areas”); 8 respondents mentioned the mapping was a key insight or learning, even though it was totally outside their field (“I was impressed with the mapping, and there was a lot of gender and nutrition issues”)”.
Taking everything into account, INGENAES program management decided to invest in further participatory collaboration mapping methodology development and application. A first seed action to be further nurtured that came out of the Lusaka conference was to use the combined CommunitySensor methodology and Kumu network visualization tool for the participatory mapping of stakeholder collaborations in Malawi. This southern African country has a very complex agricultural governance system, consisting of many intermediate hierarchical layers and organizational structures between the national and the village levels, leading to many collaboration inefficiencies. A sister project of INGENAES—both of them being implemented by the University of Illinois—is the Malawi-based SANE (Strengthening Agricultural Extension Services) project. In a joint initiative by SANE and INGENAES, a pilot was started to use participatory collaboration mapping to strengthen the District Agriculture Extension Services System (DAESS). This is Malawi’s decentralised extension framework for enabling agricultural stakeholders to enhance coordination and collaboration. The aim of the mapping was to engage in a participatory process of identifying and organizing agricultural issues for collective action within and across the governance levels.
The pilot is being co-ordinated by the Malawi conference participants who had proposed this seed action. It started a few months after the INGENAES conference, and is still ongoing. Pilot activities so far have included defining a community network mapping language based on the community network ontology described in the next sections; creating a seed map using this language to capture the essence of the national Malawi agricultural collaboration and governance ecosystem; training by the author of 1 SANE project staff and 10 Malawian agricultural extension professionals in the CommunitySensor methodology and Kumu tool; two field visits applying the methodology to local agricultural communities; a stakeholder sharing session with national Malawian agricultural organizations; and SANE continuing to use and expand the mapping approach at the regional, district, and national levels.
Key to the Malawi implementation of our participatory collaboration mapping approach is that local agricultural communities are owners of their own maps. The mapping approach is being used by agricultural coordination platforms made up of diverse agricultural stakeholder (e.g., businesses, farmers, researchers, extensionists, etc.) who map initiatives within the communities where they work. As most villages do not have electrical power, posters are used to map several local initiatives at each session, thus spanning the digital divide (Figure 4
). These initiatives are then presented to the overall session group by the community members. Symbolic connections between elements that the initiative maps have in common are made by connecting the posters with pieces of thread. The posters remain with the communities, since they are the owners of their own content. The trained agricultural stakeholders take pictures of the posters, then add the posters to the online Kumu maps when back at their local office. During their next visit, they bring prints of the revised online maps, which can be discussed and further annotated, The Kumu tool then allows for individual online agricultural community maps to be aggregated into new views, so that interesting connections and patterns in the combined maps at the higher (area, district, and national levels) can be discovered. An example could be a certain stakeholder role prevalent in many local agricultural communities, thus that role could bridge community initiatives across villages, regions, and districts, spawning further sensemaking activities.
The Malawi case is ongoing, and results are still being written up. However, we hope that—like in the INGENAES conference case—this succinct case description gives a flavor of the community network empowerment that participatory collaboration mapping can generate. To stress this point, we conclude this section with a quote from one of the ministry level representatives: “DAESS mapping provides a remarkable opportunity through which districts and DAES may easily plan and monitor the performance of the system in relation to delivery of extension services. The more people are oriented and the sooner the approach is rolled out to other districts, the more DAESS will become a force/system to reckon in the councils and at national level”.
In a globalizing world, many communities—ranging from local place-based communities to global communities of practice—are increasingly in flux and need to collaborate with one another. It is important to realize that such communities are embedded in a much larger socio-technical context, including a networked commons of overlapping stakeholder relations, goals, interactions, resources, tools, and so on. For society to address its wicked problems by building the necessary collaborative capacity towards collective impact, a much better understanding of such community networks and their socio-technical context in common(s) is of the essence. In this article, we have therefore not concentrated on what binds particular communities internally, but rather tried to shed light on their ill-understood outer context, and in particular the (potential) overlaps with other communities. In other words, our interest was in representing and understanding this networked community commons, how communities can make sense of it across their boundaries, and use these insights to increase their inter-communal collaboration.
Engaging in a participatory process of mapping the overlap between their collaboration ecosystems can help communities better understand one another and build productive bridges across their boundaries, without each community in the network losing its identity. In this article, we introduced the community network ontology that is at the heart of the CommunitySensor methodology for participatory community network mapping, described how it was distilled out of large number of participatory mapping cases, and illustrated how it is being used in practice.
The foundation of our cross-case CommunitySensor community network ontology is a community network conceptual model, consisting of element and connection types classified in categories and sub-categories. Main categories of element types are ‘Participants’, ‘Interactions’, ‘Resources’, ‘Content’, and ‘Purposes’; connection types include ‘Association’, ‘Involvement’, ‘Access’, ‘Production’, ‘Contribution’, and ‘Structural connections’. The most important application of the ontology is in the design of mapping languages tailored to the specific collaborative interests of particular community networks, but grounded in a growing general body of cross-case mapping insights. The ontology provides the conceptual structures for efficiently creating meaningful maps that matter to the community network. To do so, a community network first examines the cross-case CommunitySensor community network ontology, in order to create its own, customized mapping language. It does so by selecting relevant element and connection types, by refining and extending these types where needed, and by defining views on the map relevant to that particular community network case. Views are essentially selections of the elements and connections of a map, potentially each with a different layout. They can be used, for instance, to create highly focused and relevant discussion agendas for meetings and conferences.
Our ontology has a different purpose than ‘classical’ ontologies aimed at supporting formal knowledge representation and reasoning purposes, such as prevalent in the Semantic Web. Ours is to support human collective mapping and sensemaking purposes, with the aim to increase the collective impact of the community networks in which these people engage. To explain what this means in practice, we described one particular application of the ontology—participatory collaboration mapping for field building—and presented—in considerable detail—a case of mapping a global conference of the international and interdisciplinary INGENAES agricultural community network. We showed how we used the CommunitySensor community network conceptual model to design the tailored ontology for the INGENAES community network. The INGENAES ontology in turn was used as the language to map the collaboration ecosystem of that community network. Using this language, a map of the INGENAES collaboration ecosystem was created, with extensive input from conference participants. Those participants not only provided the data for the map and made sense of it in many different hands-on ways, but even helped refine the mapping language itself. The map shows how existing projects, organizations, wisdoms, and proposed seed actions contribute to a comprehensive network of themes representing the purpose of this emerging international and interdisciplinary field. The dense web of connections between the various elements of the map can be seen as a proxy for the collective impact jointly being realized. Of course, the maps and their views are not the territory: they are only very partial and partially accurate interpretations of that very complex real world-collaboration ecosystem. Still, being able to at least outline the contours of the common collaboration ground was considered very useful by the conference participants.
A field is not a single community with a shared identity and common set of practices, but rather a more loosely-knit network of stakeholders intersecting around various themes, projects, organizations, etc. In field building, relationships and forms of collaboration continue to expand and increase in scale [27
]. As such, field building is an interesting testing ground for developing participatory community network mapping approaches with the aim to grow new collaborations between communities towards collective impact. Such community networks are different from the ones usually described in the literature, which often focus on well demarcated communities of practice or place with a clear identity, history, and boundaries. We would argue that the more diffuse and composite community networks (in the sense of [18
]) that mesh in the case of emerging fields, like in the INGENAES case, need even more adequate sensemaking support to build a common identity, working culture, and set of practices. Sensemaking is the interplay of action and interpretation, where meanings materialize that inform and constrain both identity and action, and where types and categories being socially defined have considerable plasticity [40
]. The INGENAES case, combining a “dance of participatory mapping and facilitation” [28
], has shown both the need and a fruitful approach for making that complex and engaged kind of sensemaking towards collective impact happen. A related case further showing promising signs in that direction is the SANE-INGENAES follow-up project currently underway in Malawi, where agricultural coordination platforms actively engage in mapping their own local community agricultural initiatives, and where (aggregates of) these maps are starting to be used to make sense at the local, area, district, and national levels. In future work, we aim to expand this rudimentary conceptualization of collective impact by using insights from collective impact research and practice, social and value network analysis theory; and the large body of social innovation and community informatics work on monitoring and evaluation (e.g., [1
Using the element and connection types of our community network ontology cannot only be used to define a mapping language and relevant views on the map, but also to construct collaboration patterns used for eliciting relevant knowledge. Collaboration patterns are meaningful conceptual networks of elements and connections capturing good collaborative practices [36
]. Different communities and domains require different such patterns and practical sensemaking approaches in which these patterns are filled out and applied (like using the mapping wall in the INGENAES conference case to evolve the meanings of themes, as well as the posters used to fill out the initiative templates by the Malawi agricultural stakeholders). These patterns thus need to capture the often subtle interplay between the mapping tools and artefacts and the social mapping and sensemaking processes in which they are being created and used. Often, collaboration patterns for effective use entail a combination of the high-tech and low-tech, online and physical, synchronous and asynchronous. The online Kumu map embedded in the use of a mapping story, facilitated group processes, and the mapping wall in the INGENAES conference case, as well as the mapping and sensemaking process to use posters to capture agricultural initiatives during field visits in the Malawi case are good examples of such complex socio-technical patterns. Further inspirations for designing such hybrid collaboration patterns can be found in, for instance, the “community orientations” that summarize good practices in the socio-technical design of effectively using online tools to support community interactions like meetings, access to expertise, and community cultivation [41
]. Along similar lines, in [42
] we shared how we distilled best practice lessons out of a series of social innovation cases to form a library of re-usable collaboration patterns. A similar approach could be used in developing collaboration pattern knowledge bases for community networks.
Shifting towards a more explicit ontology engineering point of view, ontologies can range from a simple hierarchy of concepts with subsumption relations (i.e., a taxonomy) to complex networks of relationships, concepts, and constraints [24
]. Our ontology so far is a rather basic one from the point of view of structural complexity. It only contains element and connection type (sub-)categories and simple constraints that define which element types can be attached to what connection types. In future work, we also aim to include attribute values on element and connection types and visualization constraints. For example, a mandatory—or at least recommended—attribute of an ‘Organization’ element type could be a ‘Contact’ attribute, so that there is always somebody on the map who can speak on behalf of that organization. An example of a visualization constraint could be that the width of a connection between an ‘Interaction’ and a ‘Participant’ involved in that interaction indicates the strength of the connection. How to measure that strength depends on the mores of the particular community network being developed, but we also want to learn more about that from the rapidly growing data visualization literature, e.g. [43
Formalizations can facilitate the creation of more congruent understandings among participants in interorganizational relationships. Besides being a means to achieve coordination, control, and legitimacy, the right kind of formalizations are especially useful when there are large inter-partner differences and high degrees of ambiguity and uncertainty [44
]. However, what is this right level of formalization in a community network? Our participatory ontology evolution approach is very much in line with the recent trend in decentralized ontology engineering methodologies emphasizing less formal, more lightweight ontologies [32
]. The degree of formality of ontologies ranges from informal folksonomies to the very formal controlled vocabularies in use in the Semantic Web [25
], Folksonomies are collaborative categorizations using keywords freely chosen by users. Even though these keywords are sometimes completely informal—think of tag clouds—they are still ontologies, be it lightweight, dynamic, and limited in sharing scope [25
]. The CommunitySensor community network ontology can be positioned somewhere in the middle of this spectrum: community network representatives are totally free to come up with their own terms for element and connection types in their own ontologies. However, these terms are organized in a deep structure with community-specific element and connection types being classified by higher-order element and connection type (sub)categories described in the CommunitySensor community network conceptual model. The CommunitySensor community network ontology contains conceptual mapping lessons learnt in many different cases about which element and connection types work in practice. Such a grounded approach to defining a community network mapping language is essential when creating meaningful conceptual bridges across the mapping languages of potentially very different community networks. Although our ontology is only tentative, both in terms of the element and connection types and their classification into (sub-)categories, we believe that it has already shown its usefulness as a basic conceptual framework on which to build inter-communal collaborative ontology engineering approaches.
Note that we do not propose to create one semantically tightly integrated network of community ontologies, which is often the goal of traditional inter-organizational ontology engineering [45
]. Yet, when making sense across community networks, we do need some form of common language. This language is to act as an inter-lingua to define some basic conceptual common ground, and to align potential collaboration opportunities when engaged in collective sensemaking of that common ground, as we have shown to work in our cases. However, we do not need formal, fully-specified definitions of the meaning of the elements and connections in the ontology. Instead of being unambiguous formal meaning specifications, our ontological elements and connections generally act as boundary objects. Such boundary objects play a brokering role involving translation, coordination, and alignment among the perspectives of different communities coming together in a kind of meta-community [26
], which is the case in our fractalized community networks. One way to identify such boundary objects is by reflecting on what (sub)-categories the element and connection types of the mapping languages of different community networks have in common (even though the names of the types used may be very different). Such common meanings make it possible to link different community network maps by identifying conceptually similar elements and connections, think of stakeholders or activities in common. These meaningful linking pins across maps can serve as concrete starting points for (facilitated) discussion when different community networks meet for the first time, for example in a joint exploratory workshop. Starting right away from meaningful links between their own maps—well-understood by the communities that created them but often not by the other communities that try to read them—may significantly accelerate the potential for achieving collective impact [22
Collective impact processes may benefit from dedicated “backbone organizations”, whose mission it is to catalyze collective impact networks by facilitating organizations aligning numerous initiatives addressing wicked problems [5
]. However, we would argue that such centralized approaches are not enough to scale up towards collective impact. The fundamental problem in social innovation—addressing wicked social problems with a multitude of stakeholders who cannot solve these problems on their own—is not in the early stages of prompts, proposals, and pilots, but in the later stages of sustaining, scaling, and systemic change [47
]. Besides backbone organizations, we propose we also need “backbone processes”, designed out of participatory collaboration mapping and other collective intelligence approaches. Such mapping-based backbone processes may also help to increase stakeholder engagement competence, as this has been shown to benefit from the invention of new forms of stakeholder engagement that makes communication possible that may be otherwise difficult, impossible, or unimagined [48
We want to mention one example in particular of a profound, yet ambitious potential backbone process for increasing collective impact. Etzioni astutely observed that all communities have a serious defect: they exclude. To prevent communities from over-excluding, they should be able to maintain some limitations on membership, yet at the same time greatly restrict the criteria that communities may use to enforce such exclusivity. He therefore proposed the idea of “megalogues”: society-wide dialogues that link many community dialogues into one, often nation-wide conversation [7
]. Although this sounds lofty, it is also not practical, nor does unconstrained society-wide conversation necessarily led to inter-communal sensemaking. On the contrary, unfettered social media use often leads to more misinformation, polarization and division, as many individual users lack the civic online reasoning competencies to distinguish reliable from misleading information [49
]. Still, there have recently been many developments on the front of crowd-scale online deliberation support technologies, including time-centric, topic-centric, question-centric, debate-centric, and argument centric deliberation technologies [50
]. It would not be enough to just deploy these technologies and hope for the best, though. If the deliberation support provided by such technologies could be combined with participatory collaboration mapping support grounded in meaningful community network ontologies, more scalable approaches to designing focused and productive debates on selected topics with relevant participants might emerge. Participatory collaboration mapping approaches do more than just finding common ground: they help in driving for accountability and transparency, towards fair, unbiased representation of the community [51
]. The collaboration patterns of community network ontologies could represent the balanced socio-technical mix of relationships, interactions, and purposes for seeding the media configurations and conversation processes leading towards more common ground and collective impact. Such scaled-up communication and collaboration processes would also require meta-design principles to collaboratively construct the required design rationale, media and environments [23
]. Combining deliberation support technologies with participatory collaboration mapping and meta-design principles could create socio-technical “safe (or at least: safer) spaces” in a way that promotes more productive interactions between members of many different community networks than currently the case.
There is a growing body of related community informatics work that can be connected to further develop our approach. A powerful example of ICT-mediated visual approaches to community empowerment at the local level is how favela residents used cameras to bear witness of what is happening and what is needed in the slums of Brazil [52
]. Besides visually mapping a community’s lifeworld at the grassroots level, however, we also need to be able to scale up towards collective impact. Community informatics can also help developing such an inter-communal network perspective, as it views ICT not only as the carrier and facilitator of the connections within a community, but also between communities [11
] (pp. 16–17). For example, Van Biljon and Marais [51
] outline a community network ontology and participatory mapping process for research collaboration mapping and sensemaking across organizations and research communities in the domain of development informatics. Finally, participatory collaboration mapping and sensemaking are meaningless if not suffused with community norms, values, goals, and ethics, and analyzed from an emancipatory critical-interpretive point of view: the essence of the budding field of community informatics [14
]. Although this article paid a lot of attention to the more technical aspects of community network ontologies, we are very much inspired by these fundamental community empowerment principles driving our field of research and practice. We have and will continue to be guided by them in the further development of the CommunitySensor methodology.
The world is in dire need of wicked problem-solving capacity. The ecological, economic, social, and political crises keep multiplying. Organizations and communities can no longer address these issues in a top down way on their own. However, even individual community network initiatives are not enough. For truly collective impact, collective intelligence by community networks of stakeholders acting in concert is needed. Those initiatives themselves need to align and be interwoven, so that in a fractalized way, collective impact can be scaled from the bottom-up to societal transformation. For this smart scaling, ongoing inter-communal participatory mapping and sensemaking is needed, in which community network initiatives find common ground, without losing their own identity and foregoing their own interests and needs.
In earlier work, we introduced the CommunitySensor methodology for participatory community network mapping. It supports the mapping and sensemaking processes both within and across communities, in order to strengthen their network development. The main contribution of the current article is our community network ontology—with the CommunitySensor community network conceptual model at its core. The ontology is not the result of a theoretical exercise conducted in a lab, but hard-won practical knowledge that we have distilled over time out of 17 funded cases, with real interests and resources at stake. We showed how the community network ontology scaffolds the CommunitySensor participatory mapping methodology by using it to effectively and efficiently configure and customize the language of the element and connection types, map views, and collaboration patterns most relevant to the specific community network being mapped. We illustrated this potential by sharing how the community network conceptual model was used to configure the design of the map ontology at the heart of participatory collaboration mapping efforts to promote learning and collaboration for field building in a global agricultural community network. If widely adopted, such an ontological approach could significantly increase the efficacy of inter-communal sensemaking processes for collective impact.
The work we presented is far from finished. Many criticisms can be leveled against the current implementation. As a community informatics field of researchers and practitioners, we still have disagreements about the fundamental nature of community networks, and how the community and network dimensions should be defined and interrelate. The community network ontology introduced is only tentative in its classification, contents, and application. The participatory collaboration mapping methodology is very much under construction and its co-dependence with the ontology is still under investigation. We are a long way from such methodologies being able to support collective impact at the full society-transforming scale. Still, the use case examples we presented here (and many more we have left out) show that there are both a need for and promising practical ways to make an ontology-driven participatory collaboration mapping way of thinking and working work.
Although the world is facing many societal challenges, there is an enormous reservoir of good will and capacity to effect change for the common good. However, awareness of stakeholders needs, intentions, and potential collaborations is scattered. We believe that growing community network mapping and sensemaking capacity is of the essence to increase the collective intelligence needed to overcome this fragmentation of collaboration. CommunitySensor is one initiative to increase that capacity. Development of the participatory collaboration mapping methodology and supporting community network ontology are far from finished. However, we hope that sharing our intermediate results will inspire others in developing related work. The road we have to travel is still long, but we have taken more than a few steps already.