Every context is unique. Before beginning a project, especially if you are working with a historically marginalized group, take time to understand your institution's relationship with that community locally. If you're not working with a community but are working with a diverse team, know what is at stake for them.
Collaborating with Community Members
Like other fields, DH has taken inspiration from the expertise of Indigenous' studies scholars where it pertains to ethical collaboration. On the whole, many historically-marginalized communities have a rational distrust of academia, so building trust here will be necessary for any project to succeed. Both articles use case studies and offer general guidelines, but it will come down to you and your team learning the context and building relationships with your specific community.
Using the case study of Sami Knowledge Systems, Cocq discusses how indigenous studies principles provide a rubric for DH scholars to assess their own cultural biases. Applicable to all community collaboration as it is flexible and descriptive rather than prescriptive. An example of collaboration done well.
Through extensive case studies, this chapter of Data Feminism highlights the principle of "embracing pluralism" and moving away from the harmful idea of a single genius authoring a work. They argue that those with indigenous, local, and embodied experienced data should be prioritized in the work.
Through a combination of case studies and discussion of general best practices, Earhart lays out invaluable advice for DH scholars when working with marginalized communities. She discusses how communities may have existing memories of harm done by academic institutions, even ongoing harm, and how trust must be rebuilt through conversation before research can be ethically conducted.
This resource is designed for scholars working community-based research or collaboration to guide them in using informed refusal in their work. Informed refusal has three key elements. One, that community participants are aware of their ability to withdraw consent or refuse to give information at any time. Two, researchers are attuned to hesitance and other social cues that indicate refusal. Three, if data emerges that could be harmful to a community’s reputation if shared with the academy or broader public, researchers and the community carefully reflect on what they will share and what they will refuse to share.
Drawn from Kimberlé Williams Crenshaw's theory of Intersectionality, I hope these tools guide scholars in understanding how collaborators overlapping identities affect their position, power, and privilege. In order to create an equitable diverse collaborative team, it is necessary for those of us in the dominant group, such as white scholars like myself, to understand how others are pushed to the margins.
This section is short, and I hope that it inspires further research, especially situated in your own scholarly context.
Using a Disability Studies framework, Hamraie challenges DH scholars working with community-based research to consider who is in the crowd of their data. Are they pulling broadly or from those with lived expertise? The author uses a case study to exemplify their points.
McPherson lays out the history of the field of computation, segregation, and the emergence of a largely white digital humanities field. Scholars seeking to create intersectional collaboration should understand this history.
Posner argues that DH needs to utilize critical frameworks (i.e. feminist, CRT, queer theory) in order to accurately represent the world, rather than as some philanthropic exercise from dominant scholars.
Roopika discusses how principles of Black Feminism can help scholars navigate key challenges in global DH projects - from acknowledging cultural differences between collaborators to a broader move to recognize non-Anglophone work. She emphasizes the importance of situating the local details to understand a global DH community.
