Guest Blog: The “Cluster of [Positive] Discomfort”
In her guest blog, doctoral student Marion McKenzie from the University of Virginia shares her experience as a 2021 Community Fellow in Earth Science Information Partners (ESIP). Throughout the year, McKenzie assisted the Community Data Cluster. As ESIP collaboration areas are intended to spin up and spin down as projects wrap up and people move on, the Community Data Cluster reached a point where it made sense for the group to change course. McKenzie shares what she learned in the process.
ESIP clusters embrace the in-between spaces
Starting this blog post was a lot more difficult than I expected it to be. I thought writing about my experience with the Community Data Cluster would be straightforward, missing the part where writing is a large key in process[ing] (Gibbs, 2007). I started the year with the Community Data Cluster with little more than an eagerness to learn and a few experiences of community-based research under my belt. At one of the first meetings I attended, cluster co-chair Steve Diggs referred to the group as the “cluster of discomfort,” referencing the often uncomfortable conversations we would have regarding data exploitation in the community where we were working, our potential contributions to that exploitation, and civil disagreements over how to proceed because of differing opinions.
What I’ve learned from this year in the “cluster of discomfort” is that sitting in that discomfort or uncertainty for our next steps isn’t a bad thing. In fact, it’s in these in-between spaces that we learn and grow the most. As academics and scientists, we are often quick to make plans to find solutions and accomplish our tasks: Finishing those deliverables and moving onto the next task. However, in working with communities and developing community relationships, that workflow should not be followed as it often leaves out some of the most important voices from the conversation. In reality, when working with community-based data, there aren’t always clear solutions to problems, there will be disagreements, and sometimes there are more than a few ways to reach an end goal, even if it takes more time than you were expecting.
This fellowship has taught me to become more comfortable sitting in discomfort and listening: asking myself what I can learn from the process rather than the end goal.
Community building is not always straightforward
The path of this cluster was not always straightforward. After Steve’s initial contact from Data for Black Lives left Flint, Michigan (where the need for data organization was expressed) we were, as Steve put it, flying on the same trajectory without much visibility to where we were heading.
At the same time we were trying to establish contacts to solve data-related needs in the community, the pandemic had rightfully shifted everyone’s focus, both in Flint and among the cluster community. These challenges had made fostering meaningful connections between our cluster members and Flint community leaders more difficult. Personally, working with the Community Data cluster was difficult at times as I was trying to navigate an entirely virtual landscape to develop meaningful connections with a community both geographically and demographically distant from myself and my lived experience. Because the Flint community’s trauma from environmental racism is widely exploited, I was constantly aware of how any action we took as a cluster, no matter how well intentioned, could have the potential to be a harmful impact.
However, the unique thing about the Community Data Cluster that encouraged me to stay engaged were the widely different identities and experiences across our group that all brought different perspectives to the table. I had to take a step back and realize my role wouldn’t be one where I needed to directly identify with the problems facing Flint, but rather one where I could support and encourage those in the Community Data Cluster that felt connected to the community in their own way.
Fellowship is building relationships
After the loss of our original contact in Flint, we continued to work on the project but knew we would need guidance from community organizers in order to make a meaningful impact with our efforts. Attempting to develop genuine relationships across the virtual space turned out to be highly difficult, so using an on-the-ground approach, Steve flew to Flint to make introductions in real time.
This direct method of making connections in Flint turned out to be highly effective and meaningful, as we were able to get back on track because of Steve’s determination. Not only was this relationship building tactic a bit unorthodox, but our involvement in Flint was also very different from most academic approaches to community-based work: Rather than proposing to collect new data, we sought to fill a gap in what already exists, with an initial focus on simply identifying what it is that we don’t know. The landscape of data in Flint is not easily navigated without a trained eye, especially when information is scattered widely in different formats, with different measurement values and a number of organizations conducting different methods. In this way, the ESIP members involved in this group were uniquely poised to collect, assess, and organize the scattered data in order to provide the community organizers with an idea of what is known and what still needs to be addressed. Our cluster was then able to realign with Flint community organizers before the 2021 Summer Session, allowing us to directly address the issues our collaborators deemed most pertinent to our skill set. I think that’s one of the themes that needs to be present in all community-based work:
Our role as scientists shouldn’t be one of taking charge and extracting information from communities and then leaving, but rather centering community members and organizers and supporting them in leading the charge.
Filling in the gaps in Flint's water data
This idea also reiterates much of what literature on community-based research expresses (e.g., Israel et al., 2012; Duran et al., 2019; Beyond, 2019). Stemming from these conversations, the Community Data cluster conducted a privacy assessment, curation and preservation-readiness assessment and data munging of available water quality data in Flint. Rather than focusing our work in Flint as an academic exercise, which is admittedly where I feel most comfortable, the cluster pushed beyond that boundary to become more engaged with the data communication problems that exist within Flint.
Academia often supports the type of work that benefits the career of the individual, whether that be through a publication, seminar, etc. It was eye-opening to be involved in this project where academic goals were not the priority; we were navigating the concept of altruistic work to provide for a community with a data-related need. While the Community Data Cluster is going on a break once we finalize the data assessment for our community contacts in Flint, Steve intends on staying engaged with this work and we hope to continue conversations surrounding involvements in communities impacted by environmental racism with the Community Resilience Cluster. Due to the interdisciplinary member base and diverse skill-set of members, ESIP is uniquely positioned to be able to positively impact many communities through its efforts. I feel very fortunate to have found this group of people and look forward to staying involved after the end of the Community Fellowship. Stay tuned for more blog posts!
Marion McKenzie is a PhD candidate in the Ice and Ocean Group at the University of Virginia (UVA). Her research focuses on solid Earth dynamics involved in ice stabilization of the paleo-Cordilleran Ice Sheet. This work is conducted through a lens of modeling, remote data, geochronology and geochemical data analysis. Marion also serves on the UVA Department of Environmental Sciences' Diversity, Equity, and Inclusion committee and teaches a middle school course titled “The Cool Cryosphere!” through UVA's School of Education. She was a 2021 Community Fellow in ESIP, working with the Community Data Cluster.