In complex, polycentric environmental governance systems, actors may choose to collaborate with one another to reduce their collective vulnerability and enhance system function. However, collaboration can be costly, and little evidence exists for how particular collaborative forums impact the broader governance system in which they are embedded. To address this gap, we investigate the role of intermediate collaborative forums, which support collaboration among a subset of system actors, in polycentric governance systems. Empirically, we analyze the structural and functional role of an intermediate collaborative forum called the Arizona Municipal Water Users Association (AMWUA) within the municipal surface water governance network for the Phoenix Metropolitan Area (PMA) in Arizona, United States. To do this, we draw from 21 interviews with water professionals in the PMA, which we analyze through a combination of network analysis and qualitative coding. We find that AMWUA facilitates strong bonding capacities among members, allowing for streamlined bridging to the rest of the network that enhances information processing and advocacy of member needs. Our findings advance theory on the role of collaboration in polycentric systems and inform the design of collaborative institutions to improve environmental governance.
Collaborative governance has emerged as a promising approach for addressing complex water sustainability issues, with purported benefits from enhanced democracy to improved environmental outcomes. Collaborative processes are often assumed to be inherently more equitable than traditional governance approaches due to their goal of engaging diverse actors in the development of policy and management solutions. However, when collaborative water governance processes ignore issues of politics and power in their design, they risk creating or even exacerbating existing inequities. How, then, can collaborative water governance processes be designed to enhance, rather than undermine, equity? To answer this question, we first conduct an extensive review of the collaborative governance literature to identify common design features of collaborative processes, which each present potential benefits and challenges for actualizing equitable collaborative water governance. After critically discussing these design features, we explore how they are executed through two case studies of collaborative water governance in western North America: groundwater governance reform in California and transnational Colorado River Delta governance. In reflecting on these cases, we chart an agenda for future collaborative water governance research and practice that moves beyond engaging diverse actors to promoting equity among them.
This article is categorized under: Human Water > Water Governance Science of Water > Water and Environmental Change Engineering Water > Planning Water
- Award ID(s):
- 2048133
- NSF-PAR ID:
- 10467624
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- WIREs Water
- Volume:
- 11
- Issue:
- 2
- ISSN:
- 2049-1948
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Collaborative learning is a valuable skill and practice; opportunities to mentor others are critical in empowering minoritized learners, especially in STEM and computing disciplines.
School norms solidify a power and expertise hierarchy between teachers and learners and fail to productively support learners in learning from each other.
Additionally, lack of awareness about peers' knowledge is a common hindrance in students knowing who to ask for help and how.
What this paper adds
An example of a designed interface called Connected Spaces with potential to foster more inter‐student collaboration, especially outside of mandated within‐group collaboration—in the form of cross‐group help seeking and help giving.
A design based research study using actor network theory highlighting the limitations of Connected Spaces in sparking notable behaviour change among students by itself but being retooled as a teacher support tool in enabling cross‐group collaborations.
Presenting conceptions of collaboration through technologies as bridging agentic gaps and acting with new agencies in performing help‐seeking related actions.
Provoking the idea of testing emerging technologies in classrooms along with sharing our analyses and reflections with the classroom as a key idea in computing education—surfacing the gap between designed intentions and the different kinds of extra social work needed in the on‐ground success of different technologies.
Implications for practice and/or policy
Designers and researchers should create and test more interfaces alongside teachers across different classrooms and contexts aimed at supporting different kinds of voluntary collaborative interactions.
Curricula, standards and school practices should further center providing students with opportunities to engage as mentors and build communities of learning across disciplines to empower minoritized students.
Researchers engaging in design based research should consider using more posthumanist lenses to examine educational technologies and how they affect change in learning environments.
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Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways.
Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it.
What this paper adds:
Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning.
Describes how we adapted six design methods to design AI features.
Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning.
Implications for practice:
We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy.
Our work also includes a reflection on our design process and implications for future design processes.
